The Finnish centre of excellence in Randomness and Structures (FiRST) calls for applications for research and research-training jobs for the summer 2023.
The summer jobs are intended for bachelor/master thesis students but motivated applications of earlier stage students will be also considered.
The areas of research of the center of excellence are mathematical physics, partial differential equations, harmonic analysis, quasi-conformal methods and stochastics.
In order to apply please fill the form below and include in attachment a summary of the applicant's study record (epävirallinen suoritusote).
The deadline for applications is 26.02.2023 at 23:59.
To apply, please fill the application form at https://elomake.helsinki.fi/lomakkeet/121481/lomake.html
Deadline for applications is 31st January, 2023.
Planetary-system research (PSR) at the University of Helsinki comprises theoretical, computational, experimental, and observational research of Solar System objects (asteroids and comets, planets and their moons). The PSR research has close connections to geophysics, geology, space physics, as well as meteorology and is focused on asteroids (e.g., ESA Gaia mission, ESA Euclid mission, ESA Hera mission in planetary defense), comets (ESA Comet Interceptor mission), Mercury (ESA/JAXA BepiColombo and NASA MESSENGER missions), and other atmosphereless bodies, as well as the planet Earth (notably NASA DSCOVR mission). Astronomical observations are carried out, for example, at the Nordic Optical Telescope (NOT) and, in the future, with the Large Synoptic Survey Telescope (LSST). Ongoing observational and computational multiwavelength analyses also include radar studies of small bodies, the Moon, and Mercury, which provides an additional source of information to their composition and other physical properties in addition to the optical data. The PSR group runs the Astrophysical Scattering Laboratory consisting of a state-of-the-art levitator-driven scatterometer, UV-Vis-NIR spectrometer, and a polarimetric spectrogoniometer. The development of hyperspectral imaging based backscatterometer is ongoing. Laboratory and computational collaboration in X-ray fluorescence spectroscopy progresses with the University of Leicester. In more detail, the research involves forward and inverse light scattering, X-ray fluorescence, and celestial mechanics methods for accrueing knowledge on individual planetary-system objects as well as entire populations of asteroids and comets.
Within PSR, summer trainee positions are opened in the following topics:
1) Inverse light scattering methods for asteroids and other airless Solar System objects.
2) Hyperspectral camera simulation software development using Python and Blender.
3) Radar observation analysis and related data analysis software development.
The training will take place in synergy with the advances of the NASA DART, ESA Hera, and ESA Comet Interceptor missions.
Contact persons: Karri Muinonen (karri.muinonen at helsinki.fi), Mikael Granvik (mikael.granvik at helsinki.fi), Antti Penttilä (antti.i.penttila at helsinki.fi), Anne Virkki (anne.virkki at helsinki.fi)
We are looking for summer trainees with an interest in theoretical astrophysics and/or theoretical physics. In addition to theoretical work, our projects include a significant computational aspect. We encourage students interested in theoretical astrophysics and computation to join the Theoretical Extragalactic astrophysics research group for a three-month period over the summer.
This year, the projects on offer are related to the KETJU-project, which has recently received funding from both the European Research Council and the Academy of Finland. In this project the aim is to use the newly developed simulation code KETJU to model the dynamics of supermassive black holes in galaxy mergers. Using KETJU the large-scale structure of galaxies can be studied, while simultaneously resolving accurately, the small-scale dynamics close to the supermassive black holes.
1) Modelling the cosmological formation of cored early-type galaxies:
In this project the goal is to use numerical cosmological simulations to study the effect of binary supermassive black holes (SMBHs) on the central structure of massive elliptical galaxies. The interaction between the SMBH and the surrounding stars ejects stars from the centre of the galaxy and using KETJU this process can be studied in detail. However, gas cooling and central star formation contributes to increasing the central stellar density. In this project the aim is to study what is the dominant process for setting the central stellar density in massive galaxies. Good computing skills and knowledge of galaxy formation theory and galactic dynamics are advantageous for this project.
2) Gravitational wave kicks from merging supermassive black holes:
In the final stages of the merger of a supermassive black hole (SMBH) binary copious amounts of gravitational waves will be emitted. In addition, the merged SMBHs will receive a kick that depends on the orbital orientation, masses and spins of the individual SMBHs prior to the merger. Using the Post-Newtonian formalism in KETJU the gravitational wave energy spectrum as a function of frequency and the amplitude and direction of this kick can be calculated. In this project the aim is to study the dynamics of merged SMBHs in the centres of massive galaxies. Good computing skills and prior knowledge of general relativity and galactic dynamics are advantageous for this project.
3) Black hole accretion and feedback in merging galaxies
Supermassive black holes (SMBH) at the centre of massive galaxies accrete gas, thus converting efficiently the potential and kinetic energy of this gas into radiation, which heats the surrounding gas and affects the evolution of the entire galaxy. This interplay between gas and SMBHs is termed the black hole accretion and feedback process, and it is a key ingredient in modern galaxy formation models. In this project the goal is to use KETJU numerical simulations to study how the accretion and feedback process of binary SMBHs affects the properties of merging galaxies. Good computing skills and knowledge of galaxy formation theory are advantageous for this project.
4) The impact of astrophysical processes on the structural properties of forming galaxies
In this project the aim is to use outputs from cosmological KETJU simulations to study the impact of astrophysical processes on the internal structure and kinematics of forming galaxies. In particular, this project aims at understanding how galaxy mergers and black hole feedback processes can give rise to counter-rotating stellar components. In addition, different star formation quenching mechanisms (environmental, morphological, energetic) will be studied, with a focus on understanding what imprints will be left on the spatial distribution of the stars. In this project we will also study the formation and evolution of nuclear stellar components around supermassive black holes. Good computing skills and knowledge of galaxy formation theory are advantageous for this project.
When applying please indicate your preference for the research topic. All research topics could also form the basis for either a Bachelor or a Master thesis in Astrophysics, Theoretical physics or a related field. Preference will be given to students, who are working on their Master thesis. For advanced students there is also a possibility to continue with a PhD thesis project after the successful completion of the Master thesis.
For more information see:
Students interested in research on stellar magnetic activity are invited to apply for 1-3 months Summer training as research assistants. The work will be done within the SOLSTICE research project funded by the Academy of Finland. More information on the project can be found at its Wiki-page:
Preference will be given to students writing their master or bachelor thesis on stellar magnetic activity. A list of completed studies and grades should be appended to the application. The supervision will be organised by Docent Thomas Hackman, who can also give further information (e-mail email@example.com).
The goal of this project is to learn about and perform calculations in holographic duality. This duality is a discovery in theoretical physics (more specifically in string theory) that relates two very different types of theories; a gravitational theory and a quantum field theory. Results in one theory can be translated to results in the other.
This duality can be used as a tool to explore many different areas of physics. Researchers including several here in Helsinki are using it to study nuclear physics, condensed matter physics, and more.
We are searching for 1-2 summer trainees interested in learning about holographic duality as well as other areas of theoretical physics and in gaining some experience in performing related calculations. The details of the project are somewhat flexible and will be chosen considering the experience and interests of the trainee.
Niko Jokela, firstname.lastname@example.org
The Euclid group at the Department of Physics offers paid positions for one or two summer trainees in the summer of 2023.
Euclid is the cosmology mission of the European Space Agency (ESA), scheduled for launch in the second half of year 2023. The goal of the mission is to solve the mystery of dark energy, that is, what is causing the accelerated expansion of the universe. For this goal the satellite measures, on the one hand the 3D distribution of galaxies in our universe, and, on the other hand, the effect of gravitational lensing on observed galaxy shapes. One of the data processing centers of Euclid is located in Finland and operated by the Department of Physics.
The Euclid group at the Department of Physics develops data analysis methods for Euclid. The summer trainees will work on cosmological simulations related to this. The positions are suitable for students in physics, theoretical physics, astronomy, or computer science. Working on a Bachelor’s or Master’s thesis can be included. No formal training in computer science is required, but interest in programming and basic computer skills are necessary.
We pay attention on a good study record and on programming/computer skills. The following courses are a helpful: Cosmology I-II, General Relativity, and Cosmological Perturbation Theory. The courses of Galaxy Survey Cosmology (PAP 352) and Gravitational Lensing (PAP 353) provide the theoretical background for Euclid science, and it is therefore recommended that the student takes these courses in spring 2023. However, none of these criteria are strict requirements, all applications are considered.
Elina Keihänen (email@example.com)
Hannu Kurki-Suonio (firstname.lastname@example.org)
We are offering several positions which will challenge and inspire new space scientists, with topics ranging from solar eruptions such as coronal mass ejections to the complicated plasma dynamics of near-Earth space. Most projects will use either the world’s most accurate space weather simulation Vlasiator or the leading European space weather simulation EUHFORIA, both developed by members of our groups. Some projects will also include direct analysis of satellite observation datasets. Experience with Python and basics of plasma physics are a plus, but not required. Most positions provide an excellent topic for BSc or MSc theses!
More information available at https://blogs.helsinki.fi/spacephysics/2023/01/16/recruiting-summer-trainees-for-2023/
Wave storms in near-Earth space
Large-scale structures originating from the Sun cause periods of intense disturbances in near-Earth space. Some structures generate electromagnetic waves which can accelerate particles to high energies and thus pose a threat to satellites in orbit around our planet. The goal of this project is to quantify the ability of different types of solar structures at driving wave storms, using data collected in near-Earth space.
Supervisor: Lucile Turc, email@example.com
Turbulence in coronal mass ejection plasmas
A project investigating the turbulent cascade of energy in coronal mass ejection plasmas, with analysis of data from the ground-breaking Parker Solar Probe and Solar Orbiter missions. Suitable for a BSc or MSc thesis.
Supervisor: Simon Good, firstname.lastname@example.org
Unravelling the 3D structure of bursty bulk flow in the Earth’s magnetotail
The nightside of the Earth’s magnetosphere, the magnetotail, is an extremely dynamic and active region. One of the main actors in the magnetotail dynamics are bursty bulk flows, regions of enhanced and transient high plasma velocity that are thought to play a crucial role in geomagnetic storms and substorms, one of the most explosive phenomena in the context of Earth’s magnetosphere. The goal of this project is to investigate these structures, their shape in 3D and their evolution in time using the 3D Vlasiator simulation.
Giulia Cozzani, email@example.com
Minna Palmroth, firstname.lastname@example.org
Global magnetospheric convection: the known stranger
Perturbation of the return convection during the magnetospheric substorm cycle leads to the explosive redistribution of energy in the magnetotail, and the associated chain of various space weather processes. At the same time, the sources and structure of magnetospheric convection during the substorm cycle remain elusive. Are you The One, who can add the missing links to the chain of global substorm dynamics, with the help of glorious Vlasiator?
Supervisor: Evgenii Gordeev, email@example.com
The computational field theory research group is searching for summer trainees interested in particle physics and/or cosmology, and preferably also in computational methods. The positions are for 3 months, with exact dates to be agreed upon.
Our research topics include gravitational wave production in exotic particle physics processes in the very early Universe. The produced gravitational waves may be observable with the European Space Agency's LISA gravitational wave mission, scheduled for launch in 2030's. This yields a unique window to the early Universe and to the particle physics processes which can produce gravitational waves. Our research also includes the study of non-perturbative properties of various particle physics theories with numerical simulations.
The summer trainee research projects are chosen according to the experience and preference of the trainees. The research projects can form the basis for either a Bachelor or a Master thesis.
Visualisations of past research, including some by previous summer trainees, can be seen at: https://vimeo.com/user65863371
Computational Field Theory web page: https://blogs.helsinki.fi/computational-field-theory/
To apply, please fill the application form at https://elomake.helsinki.fi/lomakkeet/121481/lomake.html
Deadline for applications is 31 January 2023.
The Biological Physics group (about 25 members) at the Department of Physics, University of Helsinki has openings for 3-5 new summer job positions (in addition to positions of research assistants who are already working in our group). The summer job projects will be based on computer simulations and theory associated with molecular biophysics. The main topics focus on unveiling how membrane receptors are modulated by lipids, signaling molecules and drugs, and how impaired cellular signaling is related to emergence of disorders such as cancer, neurological diseases, type 2 diabetes, and cardiovascular diseases. The simulations shedding light on these issues combine a variety of approaches starting from quantum-mechanical calculations and extending to classical atomistic simulations and coarse-grained molecular-level considerations. All large-scale projects are linked to collaborations with top-class experimental groups in, e.g., biomedicine, cell biology, pharmacology, and structural biology.
The group is a member of the Center of Excellence in Biological Barrier Mechanics and Disease (Academy of Finland) for the period 2022-2029 and has an excellent track record in raising external funding (for instance, European Research Council, Human Frontier Science Program, Academy of Finland, Cancer Foundation, Sigrid Juselius Foundation, etc.). The key results of the group are published in leading journals of the field (Science, Cell, Nature Methods, Nature Communications, etc.). The group's work is coupled to the life science research done in the Helsinki Institute of Life Science, and the group collaborates with > 30 experimental teams world-wide.
The choice of the summer job candidates will be primarily based on excellence/skills and motivation. Experience in programming and/or simulations (either on previous courses or in practical work) is considered an advantage. Many of our project assistants wish to carry on to a PhD degree. Applicants from all universities (Univ Helsinki, Aalto, Tampere, etc.) are welcome.
Those interested are requested to apply via the Department of Physics summer job application system. Include a brief statement of research interests and motivation, CV, and an excerpt from the study rolls. If this is not possible (e.g., applicants from other universities), please feel free to contact us directly (see below).
For further information, please check the web site of our group: https://www.helsinki.fi/en/researchgroups/biophysics, and the website of our Center of Excellence (https://barrierforce.utu.fi/).
If you have any questions, please contact the director of the group, Prof. Ilpo Vattulainen, firstname.lastname@example.org
Our research group (Computational Bioenergetics Group/Sharma Research) is located at the Department of Physics, University of Helsinki (Kumpula campus). We study molecular mechanism and function of proteins involved in energy generation by using multi-scale computational approaches. We study their mechanistic aspects in great depth with extensive experimental collaborations in Finland and abroad. Our research is supported by Academy of Finland, Sigrid Jusélius Foundation, Jane and Aatos Erkko Foundation, University of Helsinki and Magnus Ehrnrooth Foundation. Some of our recent research has been published in widely read journals.
See our latest publications at - https://scholar.google.fi/citations?user=G4xsLQ0AAAAJ&hl=en
Our group webpages at - https://sites.google.com/site/vivekvivsharma/home
We are looking for 1-2 talented and motivated students for summer jobs, who are willing to work on challenging problems in computational biochemistry and biophysics. The selected student will utilize Finnish and European high-performance supercomputers to solve life-science problems associated with the molecular mechanisms of proteins involved in energy generation. He or she will learn and apply latest technologies in classical molecular simulations, quantum chemistry, hybrid QM/MM and molecular dynamics (MD), ML methods applied to MD simulations, visualization and large-scale data analysis.
Candidates should have a good track record in studies, and a very basic knowledge of physics, chemistry and biology is expected. A prior general knowledge of Linux OS, and computational tools (such as plotting software, etc) would be an asset. Any experience in modelling and simulation techniques is considered a plus, though not required.
Interested students, please apply through Department of Physics summer job application system. Include a short statement on research interests, one-page CV, and a brief transcript of studies.
It is to emphasize that many summer trainees in our group have continued towards BSc/MSc thesis projects, all of which led to peer-reviewed publications in esteemed journals. Therefore, in our highly productive research group, we not only train younger scientists, but with excellent outcomes in terms of thesis and publications.
For more information, please contact Vivek Sharma, email@example.com
The Materials physics simulation groups at the Department of Physics, University of Helsinki have openings for 2-4 summer student positions in the field of multiscale computer modelling of radiation-matter interactions, surfaces, and mechanical properties. The modelling starts from the atomic, quantum mechanical level and continues from there all the way to the macroscopic continuum level. The main methods include classical molecular dynamics, density-functional theory, kinetic Monte Carlo, binary collision approximation, electrodynamics, and finite element modelling. We also actively use the machine-learning methods to address problems in Materials Physics. Often the methods are combined in comprehensive multiscale models to improve the predictive abilities of modelling. The problems at hand are the green energy solutions by developing durable materials for fusion power plants and new materials for efficient batteries, finding new solutions for quantum computing at room temperature, nanoscale materials with new exciting properties, materials for particle colliders of unprecedented power.
The work is to be done in the large group of more than 30 members, who are active in research (more than 40 international refereed publications annually), friendly and efficient in collaborative interactions and fun and supportive socially. The group carries out the research based on materials physics simulations under the supervision of Prof. Flyura Djurabekova, Docents Antti Kuronen and Fredric Granberg, University researchers and postdocs in the group.
These groups form the simulation part of the Helsinki Accelerator Laboratory (https://www2.helsinki.fi/en/researchgroups/helsinki-accelerator-laboratory/research). In addition to carrying out active independent research within the laboratory, the groups have a broad range of international contacts with leading ion beam, fusion research, and accelerator technology groups around the world, including Big Science research activities at CERN and ITER.
We are looking for undergraduate students of both the BSc level (2nd year on with the focus on Physics studies) and of the MSc level with the interest in the fundamental Materials Physics using computational methods. The summer work can result in an exciting topic for the student’s BSc or MSc thesis. The students of the Department of Physics of the University of Helsinki are welcome to apply primarily. The interest in pursuing the studies toward the PhD is considered an advantage.
The applicants should have a good track record of efficient studies in physics. Experience in programming or atomistic simulations is considered a plus. If interested, apply via the Department of Physics summer student application system. Include a brief statement of research interests, a CV, and an excerpt from the study rolls.
Questions can be directed to Prof. Fluyra Djurabekova, firstname.lastname@example.org
Thin films — atomic-scale layers that are deposited on a surface (also referred to as substrate) to alter its properties and provide additional functionalities — are ubiquitous in modern-day technology. One example includes deposition of thin metal films (e.g., Ag, Au, Cu) on graphene and other two-dimensional materials. These films are essential for harvesting the unique physical properties of such two-dimensional materials in a wide array of switching, catalytic, and sensing devices. However, during deposition, microstructural damage (i.e., defects) may occur, which is detrimental for the device performance. In this project, you will study the fundamental processes that lead to defect generation during deposition of metal layers on graphene with the purpose of (i) minimizing defect density; and (ii) engineering defects towards creating new types of hybrid two-dimensional materials. The work has both experimental and computational aspects depending on your background and interest.
For further information, contact Prof. Kostas Sarakinos, email@example.com
There are several positron physics related openings in the Helsinki Accelerator Laboratory. The following review gives some idea of the kind of work done within the antimatter topical area: "Defect identification in semiconductors with positron annihilation: Experiment and theory", Reviews of Modern Physics 85, 1583 (2013).
There are two general related themes for summer projects, "Defect-related phenomena in semiconductors and metals” and "Modeling of positron-defect interactions and positron annihilation in solids". The detailed topic and tasks will be tailored according to the background of a successful candidate.
Experimental projects may involve using positron-emitting 22Na isotopes either directly in contact with studied samples for substrate analysis or using magnetically guided slow positron accelerators for thin film studies. Computational materials and positron physics projects involve application and/or development of atomistic density-functional or quantum many-body (quantum Monte Carlo) simulation techniques for positron-defect interactions in solids.
For further information on possible project topics, please contact the following people:
Experimental positron and defect physics: Prof. Filip Tuomisto, firstname.lastname@example.org
Theory and simulations in positron and materials physics: Dr. Ilja Makkonen, email@example.com
The attractiveness of uranium compounds is not only due to their high relevance on safety and economic performance of the nuclear power plant or the sustainability of the nuclear waste management but also from a fundamental point of view. Indeed, such materials show exciting interdependence between their properties and their complex electronic structure. Over the last decade, systematic studies of the relationship between electronic structure and ground state properties were conducted on metallic compounds providing opportunities to develop more accurate and predictive theoretical model in 5f electron physico-chemistry. However, the case of oxide UxOy compounds remains unclear.
Recently, X-ray emission spectroscopy (XES) revealed itself to have the potential to provide detailed information and quantification of 5f delocalization. However, almost nothing is known about XES on uranium compounds, essentially because of the lack of the corresponding data, such a technique being only in its infancy at synchrotron radiation facilities. The renewal of laboratory XES instruments with performance complementing the synchrotrons is providing the opportunity to fill this gap directly at laboratory.
Within the XTREME project (Helsinki Institute of Physics), this summer position aims to perform extensive and systematic XES experiments on uranium oxide samples and to interpret the resulting data based on state of the art electronic structure calculations.
For further information, contact Dr. René Bes, firstname.lastname@example.org
Application deadline 30.1.2023
Note: E-form for this position is different than for other materials physics positions. For applying, navigate to https://elomake.helsinki.fi/lomakkeet/121169/lomake.html and select vacancy: 13. Research at the synchrotron light source ESRF
ESRF (https://www.esrf.eu) is a highly sophisticated accelerator facility that produces high-energy x-rays with extremely high brilliance. The x-rays are used for studies in different fields in physics and materials science. Within the following projects the student will participate in the development of the new beamline ID20 (https://tinyurl.com/y3jj2aq9) for inelastic x-ray scattering. The beamline uses hard x-ray synchrotron methods, chiefly inelastic x-ray scattering, x-ray absorption and emission spectroscopies, and x-ray diffraction, for studying both fundamental physics, materials science, as well as real devices for catalysis, energy storage and conversion under operating conditions as well as their idealized model systems under precisely controlled environments.
The projects can and will be tailored to the student's interests and skills. The following are examples of possible projects. Please don’t hesitate to ask for our other projects as well. Our aim is that the project work would result in a Master’s thesis and a scientific peer-reviewed publication.
In this project, a very compact diffractometer for sample characterization using synchrotron beam will be designed, integrated into the control system of ESRF beamline ID20, and tested. The miniature diffractometer is based on an Advacam x-ray camera that is based on a Medipix family chip, and the control software uses Linux and Python scripts for data acquisition.
In a common lithium ion rechargeable battery, the negative and positive electrodes are typically sandwiched together with a separator – a porous membrane in which an ion-conducting electrolyte solution is embedded. For avoiding radiation damage to the electrolyte solution during synchrotron beam experiments, a flow cell will be designed where the electrolyte solution is continuously streamed through the separator. The project will consist of the design, realization and testing the cell in experiments using synchrotron light, especially in inelastic x-ray scattering experiments.
Training period 1.6. - 31.8.2023 (or as agreed)
Contact person Simo Huotari, supervisor
Tel. +358 2941 50638
The individual projects will be supervised by local researchers at ESRF.
Research fields of the Institute for Atmospheric and Earth System Research (INAR) include atmospheric aerosol particles, ecosystem-atmosphere interactions, climate change, air quality, boundary layer meteorology, hydrosphere geophysics, simulations of molecular clusters, dynamic, numeric and radar meteorology, and forest ecological studies. INAR is leading the Atmosphere and Climate Competence Center (ACCC) of the Academy of Finland.
As a summer worker at INAR you are involved in a multi-disciplinary, inspired and progressive group of researchers. Your tasks may include computer modeling and data analysis, measurement network maintenance or participating in field measurements, depending on the currently available projects and your own interests. Some students will be positioned to Kumpula and Viikki campuses in Helsinki, and some will work in Hyytiälä and Värriö SMEAR research stations. Hyytiälä is in Juupajoki in Pirkanmaa, and Värriö in Salla, Eastern Lapland.
Summer work are based on the research done at the INAR research groups. Topics for summer work include
Your summer work can be included in your studies as practical training (5 ECTS), project course (3-5 ECTS), or expanded into a BSc or MSc thesis.
More information and application instructions at the INAR website
Application deadline is 31.1.2023. Based on the initial review of the applications, some of the applicants will be interviewed in early February 2023.
For more information on the summer work application, please contact Tuomo Nieminen (email@example.com ).
Read more about HIP summer job opportunities at https://www.hip.fi/jobs-vacancies/summer-jobs/summer-jobs-at-cern/
Avdelningen för datavetenskap har över 25 lediga sommarjobb på olika forskningsområden inom datavetenskap för sommaren 2023. Ansökningstiden för de här jobben börjar onsdagen den 18 januari 2023 och slutar tisdagen den 31 januari 2023.
De här sommarjobben är i första hand riktade åt studeranden inom datavetenskap och data science. I några grupper eller projekt kan det också finnas jobb för studeranden inom matematik, statistik eller fysik. OBS! Endast studeranden inom datavetenskap och data science vid Helsingfors universitet kan skriva sin magisteravhandling i sommarjobbet eller efter det.
Sommarjobben är vanligtvis tre (3) månader långa, och arbetstiden är under perioden maj – september. När jobbet börjar och slutar bestämmas individuellt. Lönen beror på hur långt studeranden har kommit i sina studies (antalet studiepoäng), och den är vanligtvis lite över 2 000 euro per månad.
Om du vill söka ett sommarjobb, fyll i blanketten: https://elomake.helsinki.fi/lomakkeet/121760/lomake.html?rinnakkaislomake=Data_sommarjobb_2023.
Du måste bifoga ditt studieregisterutdrag (en obligatorisk bilaga) i din ansökan. Det är också möjligt att bifoga en curriculum vitae (en sida) och något annat relevant dokument i ansökan. Alla dokument måste vara PDF-filer.
Mera information om de olika sommarjobben får du från den följande listan samt av de nämnda kontaktpersonerna för varje enskilda jobb. Om du har frågor som gäller ansökningsprocessen, kan du kontakta Pirjo Moen (firstname.lastname@example.org).
Automated Reasoning / Optimization (flera positioner)
The Constraint Reasoning and Optimization group has summer intern openings for summer 2023. Interns will engage in forefront research guided by senior researchers in the group. The internship topics are related to automated logical reasoning, optimization and counting techniques for NP-hard real-world problems, ranging from theoretical analysis to practical algorithm development, implementation, parallelisation, and empirical studies, as well as novel applications of the techniques in efficiently solving real-world problems arising e.g. from AI and knowledge representation.
Automatic Deep Learning and Inference Optimization
There are several approaches for automating the design of deep neural networks (DNNs) in recent years. The main idea of this work is to automatically design new deep neural architectures and tune their associated hyperparameters. Exact optimization approaches cannot easily be used for such a NP-complete optimization problem. A wide variety of related works using specific heuristics, metaheuristics, and reinforcement learning have been proposed to confront with the problem. However, these works have lots of difficulty due to the computationally expensive of training DNNs. It is necessary to have optimal parallel solutions for DNNs training process.
In this work, the student will have chance to study how to parallelize the DNN training algorithms. We are also working on optimizing inference of these models, and the student can also contribute on this side of the research. In addition, students will do empirical evaluations of the parallel algorithms on supercomputers. As a result, the student can identify the advantage and disadvantage of developing a general optimal parallel algorithm which paves the way for further research in parallelizing programs. The real-world applications that can be considered are Natural language processing, Computer Vision, etc. The project can potentially be extended to either a Bachelor's or Master's Thesis project.
Bayesian Machine Learning
The Multi-Source Probabilistic Inference group works on statistical machine learning and artificial intelligence. We are looking for excellent candidates interested in fundamental research on approximate Bayesian inference (variational approximation, Hamiltonian Monte Carlo, differential geometry in learning, differential geometry in learning) and their applications in complex models (deep neural networks, physical simulators).
We expect you to have completed some advanced courses in machine learning (e.g., Bayesian Machine Learning or Advanced Course in Machine Learning) or statistics (e.g., Computational Statistic, Advanced Bayesian Inference), and strong mathematical skills and familiarity with deep learning frameworks are considered as merits. Both computer science and statistics students are encouraged to apply, and the topic is suitable as a MSc thesis.
Deep Learning based Visual and Inertial Sensor Fusion
Navigation systems have been developed for decades; however seamless indoor-outdoor navigation remains an unresolved challenge. At present, Global Navigation Satellite Systems (GNSS) are the superior navigation technology. Pedestrian navigation sets special challenges (size, cost, power consumption) to navigation systems. Pedestrians spend most of their time in environments where GNSS is degraded or completely unavailable, such as urban areas and indoors, and have six degrees of freedom in their dynamics.
At present, the most promising solution for seamless navigation is the fusion of GNSS, Inertial measurement units (IMUs) and computer vision. Sophisticated algorithms for fusing the measurements are essential for good solution. Traditional fusion algorithms, such as Kalman filters and their extensions, have challenges in measurement time synchronization and IMU and camera alignment, in addition to the sensor related deficiencies which add errors to the resulting position solution. Deep learning, more specifically recurrent neural networks that also consider the time dimension, provide improved fusion accuracy in addition to solving some of the challenges in sensor synchronization and calibration.
The summer trainee hired for this position will first work on setting up an existing opensource Long Short-Term Memory (LSTM) fusion framework (Clark et al. 2017) and then will join groups’ researchers on developing the existing framework further. The work requires good Python programming skills, and knowledge on computer vision and / or inertial sensor measurement processing algorithms is an advantage. The work would also be suitable as a basis for a master's thesis.
Differentially Private Machine Learning (flera positioner)
Differentially private machine learning studies learning methods that can operate while guaranteeing privacy of the data subjects. We apply differential privacy in the context of various modern machine learning methods, including Bayesian methods, deep learning and federated learning. Depending on your background, the work will combine working on the mathematical theory of differential privacy, general methods development, implementation and application of the developed methods in different applications. The topic is suitable for a Master's thesis topic.
End-to-End Machine Learning
For this position, you will study and improve the running time of ML pipelines. To do that, you will consider the various stages of ML pipelines, end-to-end (e.g., data acquisition, data sampling and processing, model training and validation, model deployment and retraining) and study how the combined efficiency of these stages can be improved.
GPU Computing for Big and Genomics Data Processing (flera positioner)
The main focus of the project is on the use of GPU computing to accelerate Big Data processing. The work can be done using the NVIDIA CUDA framework, AMD HIP framework, or alternatively using the Apache Spark distributed computing framework. The summer job is in the context of the Academy of Finland projects "Design and Verification Methods for Massively Parallel Distributed Systems (DeVeMaPa)" focusing on Big Data processing using GPUs and "Massively Parallel Algorithms and Analysis for Metagenomics and Pangenomics (MAPAMEPA)" which uses GPU processing for accelerating various genomics applications. We need new methods to support the massive increase in the amount of parallelism at all levels of the hardware/software stack. Such massive increases in parallelism will make some currently used traditional programming paradigms infeasible and thus new methods need to be devised to cope with both Genomics and Industrial Big Data use cases. The project can potentially be extended to either a Bachelor's or Master's Thesis project.
Graph Algorithms for Bioinformatics (flera positioner)
Graph as ubiquitous models in many applications domains, including Bioinformatics. In the Graph Algorithms Team of the wider Algorithmic Bioinformatics group, we can offer several topics involving applications of graph algorithms to sequencing data, for example for the assembly of RNA transcripts, or bacterial genomes. The positions are targeted mainly to MSc students at the University of Helsinki, unless there are outstanding qualifications. See below our list of available projects, and mention in your motivation letter if any specific project interest you.
Intelligent Algorithms (flera positioner)
The Sums of Products Forskningsgrupp offers summer intern positions in 2023. We work on both theory and practice of exact and approximate algorithms for hard problems. The actual research tasks will be tailored for you based on your interests and skills, and may include research on the theory of algorithms, efficient implementation of algorithms, or heuristic algorithm designs evaluated mainly empirically. Strong analytical skills are required. A successful summer internship can be continued as a Master's thesis project; continuing as a part-time research assistant can be negotiated.
Machine Learning for Enhancing New-Particle Formation Identification and Analysis
Atmospheric new-particle formation (NPF) is an important source of climatically relevant atmospheric aerosol particles. NPF is typically observed by monitoring the time-evolution of ambient aerosol particle size distributions. Due to the noisiness of the real-world ambient data, currently the most reliable way to classify measurement days into NPF event/non-event days is through a manual visualization method. However, manual labor, with long multi-year time series, is extremely time-consuming and human subjectivity poses challenges for comparing the results of different data sets. This project will develop an automatic tool to identify and analyze NPF based on feature engineering and advanced machine learning methods. The hired student will have an opportunity to interact and collaborate with world-class atmospheric scientists in INAR and work with real-world observational data generated from state-of-the-art SMEAR stations.
Programming skills (preferably in Python) and knowledge of machine learning algorithms are needed. Knowledge of atmospheric science is beneficial but not necessary. This project is planned to be extended as a Master's thesis work.
Machine Learning for Ultrasound Physics
We are looking for an intern to work in the intersection of ultrasound physics and machine learning. We collaborate with physicists working at Department of Physics on two related problems:
The concrete task depends on your interest and background and is suitable also for a MSc thesis. An ideal candidate has completed a few machine learning courses (e.g., Bayesian Machine Learning, Deep Learning, or Advanced Course in Machine Learning) and is interested in signal processing and/or physics.
Machine Learning for Virtual Laboratory in Atmospheric Sciences (flera positioner)
We have several topics in the fields of explainable AI (XAI), uncertainty quantification, and building open-source tools. While the positions are in computer science, they involve collaboration with physicists and chemists in the Virtual Laboratory for Molecular Level Atmospheric Transformations (VILMA) Centre of Excellence.
Quantum Computing for Real-Life Use Case (flera positioner)
Quantum computing is maturing. The big question about what it is suitable for remains. How do we map a real-life use case to a form that is suitable for quantum computing? Which real-life use cases are most suitable for quantum computers? What kind of quantum computers are needed to solve some use cases? The present quantum HW is not strong enough but is developing rapidly. We need a way for quantum experimentation that allows understanding what are the requirements for quantum hardware or, the other way around, what kind and size of problems a specific quantum hardware solution can handle. The key idea to answer these questions is to create a new layer to the quantum stack for modeling quantum computing infrastructure. This layer does not aim to solve the quantum problems or mirror in detail the operation of a quantum computer like simulators do. Instead, it provides a basis for what-if analysis to find out how use cases and their formulation and encoding match quantum computers.
The internship involves working on the above aspects. The work involves implementing test code, analysing and extrapolating the performance of different problem formulations, creating models for the quantum computer performance, and documenting the results in scientific papers. Applicants are expected to know the basics of quantum computing. The internship can be extended after summer as an MSc thesis worker or a part-time research assistant position.
Research in Edge Service Placement
Cloud data centers are widely deployed around the world and used to host different applications and services. These data centers already account for about 1% of the global electricity demand. This proportion is only expected to increase as more data centers are deployed closer to end-users through edge computing. Edge computing supports new latency-critical and bandwidth-intensive applications including those for connected cars, video analytics, augmented / virtual reality, and many more through a network of data centers deployed closer to users.
Our research is currently focused on placing services and applications in edge computing considering energy-efficiency metrics and integration of renewable energy into computing systems.
Since the topic is more research-oriented, it is possible that there are uncertain outcomes and the goals could be updated based on updated knowledge. It is a good introduction to research and possibilities to extend to a master’s thesis.
Supporting the Preparation of the Department’s "Sustainability in Computer and Data Sciences" Course starting in 2024
The Department of Computer Science is preparing a discipline-specific sustainability course titled "Sustainability in Computer and Data Sciences". The course will be offered for the Computer and Data Science master’s students, and the first part of it to the Computer Science bachelor’s students. The course will teach the students how computer and data sciences may impact the society, climate, and ecosystem, both positively and negatively. The first implementation of the course will be in period III in 2024 and will consist of lectures, exercises, and project works.
We are looking for a summer trainee for searching and evaluating scientific publications that will form the basis for course exercises, and for preparing interesting project work tasks which will support the students’ learning during the course. The work requires good knowledge of programming and interest in sustainability related topics. Pedagogical experience is an advantage.
Tools and Techniques for the Efficient Development of AI Systems (multiple positions)
We are looking for multiple interns to work on tools and techniques for the efficient development and operation of machine learning systems (MLOps). To ensure that machine learning systems work for real, new ways are needed to ensure their correct and efficient operation as well as their smooth development and maintenance. In particular, continuous integration (CI/CD), testing, and life-cycle support of AI systems are the focus of our research projects. The work involves implementing research prototypes to try out ideas and performing measurements. We can flexibly tailor the work to match the applicant's profile. Applicants are expected to have good coding skills. Experience in machine learning and software engineering is useful. The internship can be extended after summer as an MSc thesis worker or a part-time research assistant position.
The positions are related to the following European projects: Industrial Grade Machine Learning for Enterprises (https://iml4e.org/) focuses on MLOps, Industrial-grade Verification and Validation of Evolving Systems (https://ivves.eu/) focuses on testing of evolving systems, and VesselAI Horizon 2020 project (https://vessel-ai.eu/) on enabling maritime digitalization by extreme-scale analytics, AI, and digital twins.
Traffic Demand and Flow Estimation from Heterogeneous Urban Mobility Data
Despite the existence of diverse data sources about the urban mobility, creating realistic simulations of vehicular traffic usually requires sophisticated methods of data fusion. In frames of this study, we will tackle the problems of: (1) origin-destination matrix estimation from hourly population distribution dataset for city of Helsinki, (2) inferring traffic flows and routes within a city of Helsinki for different times of day, days of week, months and seasons using car counting measurements.
Origin-destination matrix represents travel demand between different zones within the city and is used as an input for vehicular traffic simulation. The task of the student for problem (1) will be to test and to improve existing methods of origin-destination matrix estimation using SUMO tool (https://www.eclipse.org/sumo/) to simulate 24-hour traffic in Helsinki city area. This includes reading the scientific papers about existing methods, implementing the algorithms of OD estimation and assessing the quality of traffic demand estimation with the data about actual traffic counts from Helsinki traffic counting stations. For problem (2), the student will use flowrouter tool from SUMO platform to infer traffic flows and counts given detectors data as an input. The results of the project will be used to estimate levels of CO2 emission from vehicular traffic within the city.
This project would be of particular interest for those who likes to develop algorithms, to implement practically meaningful tools and to work with real-world data. The main prerequisite is the good knowledge of Python. The work would also be suitable as a basis for a master's thesis.
User Experience of Navigation in VR
We are looking for an enthusiastic summer student who is interested in human-computer interaction (HCI) to help us conduct a user study in the area of virtual reality (VR). The goal of the project is to investigate whether techniques from cinematography can be used to improve the user experience of navigation within virtual environments. The experiments will be based on a prototype system we are currently developing in Unity for the Meta Quest 2 and Varjo VR2 Pro headsets. We expect the summer student to take an active role in performing experiments, collecting data, conducting interviews and administering surveys.
Experience and/or interest with user studies is considered an advantage. The project does not involve any programming.
Joint projects with the Department of Computer Science and CSC - IT for Science (flera positioner)
The CSC - IT Centre for Science (Finland) and the Department of Computer Science, University of Helsinki have a number of joint open positions for summer interns to contribute to projects enabling world-class science. The selected candidate(s) will collaborate closely with a CSC team working in a specific application area. The possible topics include, but are not limited to, the following areas:
Please contact Dorota Glowacka for more information about possible projects and required skills.