The University of Helsinki units of the Finnish centre of excellence in Randomness and Structures (FiRST) call for applications for research and research-training jobs for the summer 2024.
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 include mathematical physics, partial differential equations, quasi-conformal methods and (applied) stochastics.
More information can be retrieved from the FiRST website
Please notice: the call is only for students with University of Helsinki affiliation.
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 04.03.2024 at 23:59.
To apply please fill the form at https://elomake.helsinki.fi/lomakkeet/128043/lomakkeet.html
To apply, please fill the application form at https://elomake.helsinki.fi/lomakkeet/127542/lomake.html
Deadline for applications is 31st January, 2024.
Plasma astrophysics is a new, emerging research field aiming to understand the dynamics of astrophysical plasmas – hot ionized gases – from first principles. The Computational Plasma Astrophysics research group at the University of Helsinki uses theoretical and computational methods to study the most extreme plasma environments around neutron stars and black holes.
As part of our research, we also specialize in high-performance computing solutions and open-source simulation tools. To perform our numerical studies, we maintain our own computational laboratory with a dedicated in-house, >1000-core supercomputer. We use the cluster to develop the open-source plasma simulation framework Runko. The research group is funded by the European Research Council (ERC) Starting Grant project ILLUMINATOR.
We are looking for ambitious summer trainees interested in studying extreme plasma phenomena with analytical and computational methods. The projects are 1-3 months long with flexible starting dates. The projects can form a basis for a Bachelor's and Master's thesis. Available topics include, for example:
1) Theoretical analysis of relativistic plasma waves in ultra-magnetized media. The student will derive plasma dispersion relations and analyze the wave properties of low-frequency radio waves traveling in neutron star magnetospheres. Understanding the wave dynamics around neutron stars is one of the most central open questions in high-energy astrophysics. It is crucial for establishing how pulsars, rotating neutron stars, produce their observed radio emission. Good mathematical skills are required. Completion of plasma physics courses is advantageous, although not required.
2) Simulations of relativistic collisionless shocks in magnetized plasmas. The student will simulate the formation and propagation of collisionless shock with first-principles particle-in-cell simulations. Understanding the relativistic shock dynamics is important for deciphering the physics of recently discovered fast radio bursts from magnetars - strongly magnetized neutron stars. Good computational skills and previous experience with Python and/or C++ are advantageous, although not required.
3) Numerical implementation of a quantum-suppressed synchrotron emission process to the open-source plasma code Runko. The student will study the quantum synchrotron radiation process and implement a Monte Carlo method to simulate it. Quantum-modified radiative processes are crucial for interpreting the recently performed laboratory experiments of high-intensity, petawatt laser pulses. The same mechanism also controls the generation of radiation near extreme astrophysical objects like neutron stars and black holes. Good analytical and computational skills and previous experience with Python and/or C++ are advantageous, although not required.
For more information on our group and other possible research topics, see https://natj.github.io/group. Contact information: Assoc. Prof. Joonas Nättilä (nattila.joonas at gmail.com).
Turbulence, a universal phenomenon found in low-viscosity fluids, plays a fundamental role in transferring energy from large to small scales in space plasmas. This energy transfer process has been extensively studied in the solar wind, but is much less well understood for coronal mass ejection (CME) plasma. CMEs are vast eruptions of plasma from the Sun, and contrast to the more continuous outflow of the solar wind. In this project, you will investigate the properties of turbulence in CMEs and compare them with the turbulence properties of the solar wind. Cutting-edge data from the Parker Solar Probe and Solar Orbiter spacecraft, now approaching distances very close to the Sun, will be used. The project may involve (i) a mix of data analysis and theory or (ii) modelling and theory, depending on the interests of the successful applicant. The work performed would be suitable for either a BSc or MSc thesis, and funding is available for up to 3 months during summer 2024.
Supervisor: Simon Good, simon.good@helsinki.fi
Project: INERTUM
Fast collisionless shocks are one of the most dramatic manifestations of solar activity in interplanetary space. They develop from the steepening of fast magnetosonic waves and are characterised by discontinuous increases in the magnetic field strength, bulk solar wind speed, temperature and density. In the solar-terrestrial environment, fast shocks can accelerate charged particles to very high energies (several tens of MeV), posing a significant hazard to satellite technology and human activities in space. This project will involve the development of a database of shocks observed by the Parker Solar Probe and Solar Orbiter spacecraft, and adding this database to the ipshocks.fi website maintained at the University of Helsinki. Testing of machine-learning tools to identify the shocks in the spacecraft data may also form part of the work. This project would be suitable for a candidate with an interest in programming and data analysis; a strong aptitude in Python is a prerequisite. Funding is available for 2-3 months during summer 2024.
Supervisors: Juska Soljento, juska.soljento@helsinki.fi; Simon Good, simon.good@helsinki.fi
Coronal holes (CHs) are considered as the primary source of open magnetic flux emanating from the Sun and filling up the interplanetary space. They are observed in Extreme UltraViolet (EUV) wavelengths as dark patches on the solar disc and in white-light corona observations as less bright areas due to their lower density. CHs are often considered as areas with potential magnetic field structure, meaning they are free of currents and evolving very slowly with time. Contrary, Active Regions (ARs) are characterized by non-potential and rapidly evolving magnetic fields. When ARs are present at the boundaries of CHs they can affect the CH structure, via reconnection, and therefore they impact the open flux associated with that CH. A current topic of investigation is to understand the extent of this and how it affects the interplanetary magnetic field. During the summer internship the candidate will build a database of CH-AR pairs and study the possible impacts the AR (and AR emergence) had on the CH characteristics and open flux. This will be done via analysing observations and if time allows via simulations.
The research is part of the SOFTCAT project funded by the Research Council of Finland (Academy Research Fellow grant No 355659).
The project is suitable for BSc and MSc thesis.
For more details please contact the PI of the project Dr. Eleanna Asvestari (eleanna.asvestari@helsinki.fi)
The unprecedented measurements conducted by the NASA Parker Solar Probe mission have revealed the near-Sun young solar wind to be intensely dynamic. Rather than a continuous flow, the young solar wind is organized into patches of distinct smaller streams that appear to be correlated with supergranulation cells at the solar surface. Signatures of this structuring is seen not only in the magnetic field and plasma flows, but also in the behavior of particle populations at high energies. The aim of this project is to analyze and characterize the properties of the young solar wind using magnetic field, plasma, and energetic particle measurements from the Parker Solar Probe. This project is particularly suitable for candidates with a background in data analysis, spacecraft measurements and solar wind physics. Familiarity with Python and its ecosystem is a prerequisite. Funding is available for 2-3 months during summer 2024.
Main supervisor: Jens Pomoell, jens.pomoell@helsinki.fi
Project: SWATCH, FORESAIL
University of Helsinki Space Physics team (https://blogs.helsinki.fi/spacephysics/) is looking for a summer trainee (preferably one who would be willing to do a BSc or MSc thesis on the topic) to analyze spacecraft observations in the Earth’s magnetosphere. The focus would be on Van Allen radiation belts that are doughnut-shaped regions of high energy charged particles surrounding our planet. The belts have key importance for satellites in orbit and atmospheric chemistry, but their dynamics is still largely unpredictable. The work pertains to the Finnish Centre of Excellence in Research of Sustainable Space (FORESAIL). Knowledge of (space) plasma physics and Python are considered a plus.
More information: Emilia.Kilpua@helsinki.fi
Vlasiator is the world’s most accurate space environment model that has recently been added with a capability to couple the modelling of the near-Earth space to the ionised upper atmosphere, where aurora are visible. In the coupled modelling, we are now interested to understand whether the modelled ionospheric conductivities are within the same ballpark as in nature. The trainee will work with ionospheric measurement event list, and look for events that have occurred within similar conditions as the initial Vlasiator runs with the ionospheric module coupled to the near-Earth space modelling. The target is to find such event intervals during which it is favourable to compare Vlasiator results with observations. The student will need to know python plotting routines to succeed in the job. For more information: Minna.Palmroth@helsinki.fi
Summer trainee for understanding near-Earth space energy transfer. Earth’s magnetic field forms a magnetosphere that surrounds our planet and creates space weather, the conditions that can endanger technological systems and even human health. One of the most crucial aspects in understanding space weather is to understand how energy enters the magnetospheric domain in the dayside of our planet. Here, a process called reconnection is most crucial. The project is to utilise the world’s most accurate space environment simulation Vlasiator to locate the dayside reconnection line at the outer edge of the magnetospheric domain, the magnetopause. The results will be used in a publication. For more information: minna.palmroth@helsinki.fi
Interplanetary coronal mass ejections (ICMEs) are the interplanetary manifestation of CMEs which are launched from the Sun as gigantic clouds of magnetized plasma. When an ICME travels sufficiently faster than the ambient solar wind, a shock wave develops ahead of the ICME ejecta. The turbulent region of hot and compressed plasma between the shock and ICME is known as the sheath region. Compressed and turbulent sheath regions that embed strong magnetic fields have important space weather consequences as many CME driven storms are reported to be purely sheath induced storms. As part of this summer project, a candidate will build the database of sheath induced geomagnetic storms and will investigate the distinct plasma properties of sheath regions in relation to their geo-effectiveness. The project is well-suited for a candidate with an interest in observational data analysis and programming. The work performed would be suitable for either a BSc or MSc thesis, and funding is available for up to 2 months during summer 2024.
Supervisor: Ranadeep Sarkar (ranadeep.sarkar@helsinki.fi)
We are searching for summer trainees interested in the research that is done at the Detector Laboratory of Helsinki Institute of Physics and University of Helsinki. Our current activities include among others searches of magnetic monopoles and other exotic particles at the LHC, characterization of room temperature semiconductor materials, development of gamma-ray measurement tools and more recently measurements with high-altitude balloons.
Selected trainee could work for instance on development of simulation models, measurements of radiation, characterization of sensors, machine vision and optical imaging, or algorithm development. All topics could also form the basis for either a Bachelor or a Master thesis.
More information:
Matti Kalliokoski, matti.kalliokoski@helsinki.fi and our website www.hip.fi/detlab.
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 and the SIBELIUS project, which have recently received funding from both the European Research Council and the Academy of Finland. KETJU is a newly developed simulation code 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. SIBELIUS employs novel constrained simulations to accurately reproduce the Local Universe and test models of cosmology and galaxy formation.
1) The role of stellar feedback in supermassive black hole mergers
The aim of this project is to make use of the KETJU code to explore the influence of stellar feedback on supermassive black hole (SMBH) mergers. High central stellar densities in galaxies play a key role in driving binary SMBHs to merge, with energy being carried away from the binary via the ejection of stars. While this mechanism is well understood, the manner in which SMBH binaries are influenced by stellar feedback has yet to be investigated. This project will make use of a suite of idealised galaxy merger simulations with varying stellar feedback prescriptions to constrain how this feedback influences SMBH merger timescales. Proficiency in high performance computing and understanding of galaxy formation theory are beneficial for this project.
2) 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.
3) 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.
4) The possible origin of the Local Group
We will draw present-day analogues of the Local Group from the UCHUU simulation, one of the world's largest cosmological simulation, and trace them back through cosmic time to explore possible formation scenarios, identify key scenarios, and study the likelihood of various events, including major mergers in the history of the Milky Way or Andromeda, possible past interactions between them, or the exchange of satellite galaxies. Good computing skills (especially python) are advantageous for this project. Related works from the group: Sawala, Teeriaho & Johansson, MNRAS 2023; Sawala et al., MNRAS Letters 2023.
5) Discovering galaxy clusters using constrained simulations
We use an ensemble of constrained simulations of the Local Universe to search for galaxy clusters hiding in the "zone of avoidance". Obscured by the disk of the Milky Way, the zone of avoidance cannot be observed using optical telescopes. However, the SIBELIUS constrained simulations predict the most likely structures that form in this region, given the observed distribution of structures outside of it. In this project, we will use an ensemble of constrained simulation to identify persistent structures within the zone of avoidance, and perhaps discover previously unknown galaxy clusters that exist in the real universe. Good statistics and computing skills (especially python) are advantageous for this project. Related works from this group: McAlpine et al., MNRAS, 2022; Sawala et al., Nat. Astron., 2023.
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: https://www.mv.helsinki.fi/home/phjohans/group-website/research/
Contact persons:
Planetary-system research (PSR) at the University of Helsinki comprises theoretical, computational, experimental, and observational research of Solar System objects, such as asteroids, comets, planets, and planetary satellites. The PSR research has close connections to geophysics, geology, space physics, as well as meteorology and is focused on asteroids (e.g., ESA Gaia and Euclid missions, 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 (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 about 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 a backscatterometer based on hyperspectral imaging is ongoing. Laboratory and computational collaboration in X-ray fluorescence spectroscopy progresses with the University of Leicester. 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) Direct and inverse light scattering methods for asteroids and other airless Solar System objects.
2) Hyperspectral camera simulation software development using Python and Blender.
The training will take place in synergy with the advances of the ESA Hera and Comet Interceptor missions as well as the ESA/JAXA BepiColombo mission.
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
To apply, please fill the application form at https://elomake.helsinki.fi/lomakkeet/127542/lomake.html
Deadline for applications is 31 January 2024.
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, ilpo.vattulainen@helsinki.fi
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, vivek.sharma@helsinki.fi
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, flyura.djurabekova@helsinki.fi
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, kostas.sarakinos@helsinki.fi
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, filip.tuomisto@helsinki.fi
Theory and simulations in positron and materials physics: Dr. Ilja Makkonen, ilja.makkonen@helsinki.fi
The proposed work will focus on demonstrating the potential of the X-ray emission spectroscopy performed at the laboratory for the study of 4d and 5d metals as an alternative to X-ray absorption spectroscopy for material studies. The work involved experiments and theoretical calculations alike.
The main objectives is to build and evaluate through ray tracing simulation and experiments the performance of an emission spectrometer dedicated to extended EXAFS measurements, having sufficient resolution for the separation of fluorescence lines (<15 eV) and greater detection efficiency (x2 or more) than existing spectrometers.
This project aims to develop and evaluate alternative detection solutions for laboratory scale XAS experiments, with capabilities ensuring the separation of Bragg's harmonics (i.e. < 500 eV), good efficiency and transmission properties (< 10% of photon loss), and a large area (about 3-4 cm2). This project is in collaboration with the detector laboratory of the Helsinki Institute of Physics.
For further information, contact Dr. René Bes, rene.bes@helsinki.fi
Summer job opportunities at European Synchrotron Radiation Facility (ESRF, Grenoble, France) via HIP's summer trainee program. Note: The application for these projects is to be done via HIP summer trainee application form!
A) Electronic structure of f-electron systems
The project's goal is to advance the fundamental understanding of f-electron systems (specifically lanthanides and actinides) by assessing their electronic structure. The project will consist of x-ray spectroscopy experiments at the Rossendorf Beamline BM20 of ESRF and analysing the data with the help of electronic structure calculations.
B) Electrochemical flow cell
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. The project will consist of the design, realization and testing a new type of flow cell in experiments using synchrotron light at ESRF beamline ID20.
C) X-ray spectroscopy on fuel cell materials
In fuel cell catalysts, the performance is closely tied to the active sites. X-ray spectroscopy can help identify and characterize these active sites, providing information on the electronic and geometric structure that influences catalytic activity. This information is crucial for understanding the chemical reactions that take place in fuel cells, where oxygen reduction and evolution reactions play a central role. The project consists of experiments to characterize the electronic structure of oxygen in a fuel cell catalyst at ID20, perform data analysis and learn how to simulate the spectra using electronic structure calculations.
Contact person Simo Huotari
Tel. +358 2941 50638
Email: simo.huotari@helsinki.fi
Are you interested in working with novel nanomaterials and even getting a topic for your BSc or MSc project? If the answer is yes, consider applying for the summer job focusing on two-dimensional nanomaterials at the Helsinki Accelerator Laboratory. In this project you will focus on creating two dimensional materials and modifying their structure with ultra-low energy ion implantation to create new functionalisations. The project is part of a larger research project, where we create new nanomaterials with selected properties that are targeted for specific purposes, such as nanocatalysis and optical activity.
To apply, use the e-form and indicate your interest in the open text box (note: the position is not listed in the menu)
For further information, please contact Harriet Åhlgren harriet.ahlgren@helsinki.fi
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), Flagship of the Research Council of Finland.
More information on summer work positions and application instructions are at the INAR website
https://www.helsinki.fi/en/inar/education/inar-summer-jobs
Application deadline is 31.1.2024. Based on the initial review of the received applications, some of the applicants will be interviewed in early February 2024.
For more information on the summer work application, please contact Tuomo Nieminen (tuomo.nieminen@helsinki.fi).
Read more about HIP summer job opportunities at https://www.hip.fi/jobs-vacancies/summer-jobs/summer-jobs-at-cern/
The Department of Computer Science offers around 20 salaried internships in multiple research areas for summer 2023. The application period begins on Monday the 15th of January 2024, and the application deadline for these positions is on Wednesday the 31st of January 2024.
These internships are primarily aimed for computer science and data science students. In some groups or projects, there can also be internships for students of mathematics, statistics or physics. Note that making a Master's thesis during or after the internship is only possible for the students of computer science and data science students at the University of Helsinki.
The internships typically span three (3) months between May and September. The exact start and end date will be decided in individual negotiations. The salary of a summer intern depends on the phase of her/his studies (the number of credits), and it is usually a little bit more than 2 000 euros per month.
To apply for an internship, use the following electronic form: https://elomake.helsinki.fi/lomakkeet/127627/lomake.html?rinnakkaislomake=CS_summer_jobs_2024.
Applicants must upload a study transcript (a list of passed exams and courses; a compulsory attachment) and, optionally, a one-page curriculum vitae and one other relevant document with the application form. All the attachments must be in a pdf format.
Send your possible questions related to the application process to Pirjo Moen (pirjo.moen@helsinki.fi). More information on the positions themselves from the descriptions and the named contact persons below.
Application of AI methods to biological problems in the context of microbial evolution and antimicrobial resistance (multiple positions)
The positions are part of the “Multidisciplinary Center of Excellence in Antimicrobial Resistance Research” which is one of the units selected by the Academy of Finland in Finnish Centers of Excellence in Research 2022-2029 call. We will use various data analysis methods, e.g. evolutionary theory based inference, machine learning, symbolic regression, reinforcement learning, to study how antimicrobial resistance develops. This theme is at the interface of microbial evolution and AI-driven data science.
Automated Reasoning and Optimization (multiple positions)
The Constraint Reasoning and Optimization research group invites summer intern applications from MSc/BSc students in particular interested in algorithms for NP hard optimization problems and automated logical reasoning. Interns will engage in forefront research guided by senior researchers in the group. Topics include automated reasoning, optimization and counting techniques for NP-hard real-world problems, ranging from---depending on the background of the intern---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.
Bayesian Machine Learning
We work on foundations of Bayesian machine learning, developing for instance more efficient inference (learning) algorithms, neural networks that can better represent uncertainty, and ways of incorporating more useful prior information into machine learning models. We are looking for an intern with strong mathematical and statistical background to participate in research in this general area, and the topic can be used also for a MSc thesis.
Differentially Private Machine Learning (multiple positions)
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.
Explainable Machine Learning for Modelling Boreal Forests
This project is applying machine learning to better understand how CO_2 is captured from the atmosphere by boreal forests, and what can we potentially say about the effects of climate change has on these forests. In particular, we are applying machine learning methods to predict net ecosystem CO_2 exchange (NEE) based on site information and climatic parameters. Using Explainable Artificial Intelligence methods, we are comparing the most important input parameters chosen by the models. In addition, we analyze the dependencies of NEE on input parameters against existing theoretical understanding on NEE drivers.
The task of the student is to help the researchers of the project in developing the machine learning models and using explainable AI techniques to extract a better human understanding of CO_2 fluxes in boreal forests.
For preliminary work done in the project, see: Ezhova, E., Laanti, T. M. et. al: "Explainable machine learning for modelling of net ecosystem exchange in boreal forest." https://doi.org/10.5194/egusphere-2023-2559.
GPU Computing for Big Genomics Data Processing (multiple positions)
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 project "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 (multiple positions)
Graphs are ubiquitous models in many application domains, including Bioinformatics. In the Graph Algorithms Team of the wider Algorithmic Bioinformatics group, we can offer topics involving applications of graph algorithms to sequencing data, for example for the assembly of RNA transcripts, or bacterial genomes. We can also offer topics closer to algorithm engineering for graph problems motivated by Bioinformatics. A strong programming background and experience with algorithm engineering is a plus.
Green algorithm development in LUMI using reinforcement learning
As the climate change progresses and global temperatures rise, carbon emissions and power consumption of AI development have been called to question. In a field of green computing, eco-friendly and power-efficient approaches to reinforcement learning algorithms’ development are investigated and analysed. In practice, this is achieved by efficient design choices and optimization of the algorithm.
With a usage dataset gathered from the supercomputer LUMI, existing algorithms’ usage of computing resources can be analysed, and a new design paradigm can be developed. An approach well-suited to this optimization task is the hierarchical multi-objective reinforcement learning, which can account for multiple goals of AI algorithm development while maintaining the hierarchical nature of programming production. Reinforcement learning is a paradigm of machine learning that studies decision-making via reward-maximising AI.
In this position, you will be responsible for analysing LUMI usage data and supporting the development of a new green RL algorithm development paradigm in collaboration with reinforcement learning scientists. This project would be of particular interest for those who like 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.
Intelligent Algorithms (multiple positions)
The Sums of Products research group offers summer intern positions in 2024. 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.
Software development and possibility to join method development for reinforcement learning based traffic simulation
At present, transport in EU contribute to 1/4 of the greenhouse gas (GHG) emissions, and road transport is the biggest emitter accounting over 70% of all GHG emissions from transport. In Finland, transport is the second largest sector for GHG emissions, representing 21% of emissions, and the road transportation covers 94 % of the emissions in the transport sector in 2019. Road transport causes also air quality to deteriorate as well as affects the liveability of the city.
Our two Research Council of Finland funded projects develop novel reinforcement learning algorithms for improved traffic simulations addressing the sustainability of future cities from environmental and social viewpoints. We would need support in coding the algorithms into the SUMO (https://eclipse.dev/sumo/) simulation platform.
In this position, you will be responsible in coding the novel RL algorithms into the SUMO platform and within your experience also contributing to the development of the RL algorithms. The main prerequisite is the good knowledge of Python. The work would also be suitable as a basis for a master's thesis if the development of the RL method was included.
Uncertainty quantification, robust explainable AI, and open-source software for science (multiple positions)
We have positions for one or more summer interns, depending on applicants and their interests. We offer a project on uncertainty quantification for machine learning for theoretically oriented students and a project on robust explainable artificial intelligence and open-source software tools for science. The summer internship can, on mutual agreement, later continue to an MSc or even a PhD thesis.
Virtual Laboratories and AI-assisted research (multiple positions)
Virtual Laboratory is an AI-driven environment combining physical and simulated measurements with human researchers, designed to assist the scientific discovery process (see https://fcai.fi/virtual-laboratory). We are looking for an intern to work on either the general infrastructure needed for facilitating AI-assistance in general (software, active experimental design algorithms, models of human researchers, etc.), and/or for AI-assistance of research tasks in pharmaceutical research and drug discovery.
VR Exploration of Data Distributions
The goal of the internship is to create an application for the VR headset Meta Quest 2 (MQ2; previously known as Oculus). The application is meant to visualize a given data distribution and enable interactions between the user and the data (for example, rotation, zooming, data selection, etc.) The supervisor will provide a headset for the needs of the internship. The topic requires excellent engineering skills.
Inorganic materials chemistry research groups HelsinkiALD and Camargo Lab are looking for research assistants for the coming summer. The groups work with nanomaterials: nanoparticles, nanofibers and thin films. These can be used, for example, in catalysis, microelectronics, batteries and fuel cells, solar cells and optics. The summer job can be included as credits in your study programme (if applicable).
You can apply if you have studied more than 30 credits of chemistry.
Your application should include at least:
THE APPLICATION PERIOD ENDS 23.2.2024 at 18:00.
Send your application as a pdf attachment to Miia at miia.mantymaki@helsinki.fi.
The Reaction Kinetics research group invites applications for summer jobs in 2024. The starting date and length of the contract are negotiable. For further inquiries, please come to discuss the project with Arkke Eskola (Head of the Reaction Kinetics group), room B429, or send him an email to arkke.eskola@helsinki.fi.
Please apply by 29.2.2024 by sending an e-mail to arkke.eskola@helsinki.fi.
The research interests of the group focus on reaction kinetics and oxidation chemistry in gas-phase under low-temperature combustion and atmospheric conditions. In atmospheric chemistry, we are especially interested in and focused on the kinetics of Criegee intermediates. See also https://www2.helsinki.fi/en/researchgroups/reaction-kinetics. We are part of VILMA Centre of Excellence, see https://wiki.helsinki.fi/display/VILMA.
2-month internship in the Environmental Geochemistry group
Background
Ongoing research in the environmental geochemistry group of Tom Jilbert aims to investigate the retention of nutrients (carbon, nitrogen, phosphorus) in modified drainage systems in agricultural areas of southern Finland. Two-stage channels are a relatively new nature based solution (NBS) aimed at retaining sediment during flooding periods while allowing free drainage during baseflow conditions. A recent MSc thesis from the group (https://helda.helsinki.fi/server/api/core/bitstreams/89ed9ba2-2cf7-4e74-97c8-98f64529ae35/content) demonstrated the potential of two-stage channel floodplains to retain nutrients through sedimentation of organic material and sorption of nutrients from the dissolved phase. The work was carried out as part of the larger project Valumavesi, funded by the Ministry of the Environment and co-ordinated by partners at SYKE.
Salaried internship
In summer 2024 the sampling and analytical work of the Valumavesi project will continue. The environmental geochemistry group is offering a 2-month paid internship during July-August 2024 (€1700 per month gross), during which the intern will collect field samples of sediment traps from the study sites, and perform physical and chemical determinations on the sampled sediment materials in the Hellabs at Kumpula campus and through collaboration with partners in Viikki. For students of the Geology and Geophysics MSc program, the internship can be used to complete credits for GEOM1004 (5 ECTS).
Application deadline is 31 March 2024. Please send your application to tom.jilbert@helsinki.fi
For more information, please contact tom.jilbert@helsinki.fi
Visit the website of the Environmental Geochemistry group: https://www.helsinki.fi/en/researchgroups/environmental-geochemistry