Sommarjobb på fakulteten
Matematisk-naturvetenskapliga fakulteten erbjuder årligen flera sommarjobb i olika forskningsgrupper. Läs mer om arbetsmöjligheterna och sök det som intresserar dig. Arbetsannonserna är på engelska.

To apply, please fill the application form at https://elomake.helsinki.fi/lomakkeet/109361/lomake.html

Deadline for applications is 31 January 2022.

 

Theoretical Extragalactic astrophysics

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 theory 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 simulations. 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 tend to eject 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. Finally, 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. 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: http://www.helsinki.fi/~phjohans/Site/Group.html

More information:

Prof. Peter Johansson, peter.johansson@helsinki.fi

Dr. Shihong Liao, shihong.liao@helsinki.fi

Dr. Dimitrios Irodotou, dimitrios.irodotou@helsinki.fi

 

                 

Space physics

The UH Space Physics Group is recruiting summer trainees! 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 compilated 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!

A small selection of possible topics are listed below. Please indicate in your application any preference between modelling, observations/data analysis or theory. If you want to apply for a specific project, you can include that information as well. Also, please indicate if you would like to do your BSc or MSc work based on your summer trainee work.

  • Turbulence in coronal mass ejection plasmas (BSc/MSc)
  • Radio Observations from Coronal Mass Ejections on the Sun (BSc/MSc)
  • Heliospheric modelling of coronal mass ejections and background solar wind (BSc/MSc)
  • Parameters of Coronal Mass Ejections (BSc/MSc)
  • Investigating radiation belt electrons (BSc/MSc)
  • Investigating wave activity caused by solar storms in near-Earth space (BSc/MSc)
  • Understanding the impact of the most extreme space weather (BSc/MSc)
  • Sudden plasma eruptions in the Earth's magnetotail (BSc/MSc)
  • Energy transfer into the Earth's magnetic domain from the Sunward direction (BSc/MSc)

More detailed descriptions of these projects can be found at our blog at https://blogs.helsinki.fi/spacephysics

 

The University of Helsinki Space Physics Group is a leading European space physics team specialised both in observations and modelling of space plasmas. For example, we develop the novel global hybrid-Vlasov simulation Vlasiator and have a strong focus on solar eruptions. We have a dynamic and international research group with currently about 40 members including three professors and several post-docs and PhD students. Our teams lead and participate in the Centre of Excellence in Research of Sustainable Space. For more information, please visit:

 

More information:

Markus Battarbee, markus.battarbee@helsinki.fi

 

Kinetic theory of charged relativistic particles in the Earth's radiation belts

The Earth’s radiation belts are the site of acceleration for electrons reaching velocities comparable to the speed of light. Generation of relativistic electrons constitute a threat to satellites and an open fundamental problem for a wide range of astrophysical plasmas in which particles are confined by large-scale inhomogeneous fields. One of the two dominant frameworks to quantify particles' acceleration, radial diffusion, relies on qualitative arguments that violate basic conservation laws, such as the conservation of particles. In this project, we will remedy the shortcomings of radial diffusion by building a more systematic algorithm for calculating diffusion coefficients. The process to achieve this, consists in solving kinetic problems with scale separation — a useful skill from the toolbox of theoretical physics.

The project is suitable for a BSc and MSc thesis.

More information:

Markus Battarbee, markus.battarbee@helsinki.fi

 

Holographic duality and its applications

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.

More information:

Oscar Henriksson, oscar.henriksson@helsinki.fi.

 

Computational Field Theory

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 the next decade. This yields a unique window to the early Universe and to the particle physics processes which can produce gravitational waves.

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

 

More information:

Mark Hindmarsh mark.hindmarsh@helsinki.fi  

Kari Rummukainen kari.rummukainen@helsinki.fi

David Weir david.weir@helsinki.fi

Daniel Cutting daniel.cutting@helsinki.fi

Deanna Hooper deanna.hooper@helsinki.fi

 

Or visit our website:

https://blogs.helsinki.fi/computational-field-theory/

 

Materials physics plays an extremely important role in modern science and society. Our research is highly international and we actively use, and collaborate with, large-scale international research facilities such as ESRF, CERN and ITER. There is a large variety of topics within our experimental and computational research, such as nanostructures, biological matter and energy materials. Our research groups are:

Open positions

Deadline for applications is 31 January 2022.

How to apply: Fill in this e-form.
 

Biological physics

Physics of Molecules Creating Life

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

 

Computational biochemistry and biophysics

Our research 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 the Academy of Finland, the Sigrid Jusélius Foundation, the University of Helsinki and the 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, visualization and Big Data science.

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, 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

 

Computational materials and nanophysics

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 used are molecular dynamics, density-functional theory, kinetic Monte Carlo, binary collision approximation, electrodynamics, and finite element modelling.

The work is to be done in the large (more than 30 members) and very active (more than 40 international refereed publications annually) closely collaborating materials physics simulations groups of Profs. Kai Nordlund and Flyura Djurabekova and Docents Antti Kuronen and Fredric Granberg. 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.

The positions are intended primarily for undergraduate students of the Department of Physics of the University of Helsinki, who have an interesting in continuing research in materials physics at least to the MSc and possibly the PhD level.

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. Kai Nordlund, kai.nordlund@helsinki.fi

 

Thin films

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. In this project, you will work on understanding the way by which various substate materials affect the microstructure and morphological evolution of thin metal films (Au, Ag, Cu) synthesized by a physical vapor deposition technique known as magnetron sputtering. The project is experimental and entails thin-film synthesis and characterization with respect to film microstructure, composition, electrical, and optical properties. The relevance of the obtained results for devices in the areas of energy generation and saving will also be explored.

For further information, contact Prof. Kostas Sarakinos, kostas.sarakinos@helsinki.fi

 

Positron and defect physics

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
Theory and simulations in positron and materials physics: Dr. Ilja Makkonen, ilja.makkonen@helsinki.fi

 

X-ray Laboratory

Uranium electronic structure in UxOy compounds by L-edges X-ray emission spectroscopy

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 radiations 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, rene.bes@helsinki.fi

Are you an undergraduate student interested in summer work at INAR in multi-disciplinary aerosol, atmospheric physics and chemistry, ecosystem and geophysics research?

Research fields of the Institute for Atmospheric and Earth System Research (INAR) include climate change, atmospheric aerosol particles, boundary layer meteorology, ecosystem-atmosphere interactions, 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

  • aerosol physics;
  • duty operations of atmospheric and ecological measurements in Hyytiälä, Värriö and Helsinki SMEAR stations;
  • measurements and instrument development on ecosystem and atmospheric observations;
  • simulations of formation of molecular clusters, ice and gas bubbles
  • application of machine learning to atmospheric molecules and clusters;
  • atmospheric aerosol measurements and data analysis;
  • aerosol-cloud-climate interactions;
  • surface-atmosphere interaction above different ecosystems including lakes, forests, urban areas, agricultural fields and wetlands;
  • numerical modeling of atmospheric processes and weather;
  • analysis of ocean and ice sheet interactions;
  • climate change and biodiversity research in boreal ecosystems;
  • experimental tree ecophysiology, soil and lake ecology measurements.
  • carbon sequestration, greenhouse gas fluxes in agricultural, forest and peatland ecosystems.

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.

If you are interested in summer work at INAR, please apply by 31.1.2022 by filling in the e- form https://elomake.helsinki.fi/lomakkeet/109361/lomake.html Include in your application as attachements

Based on the initial review of the applications, some of the applicants will be interviewed in February 2022.

For more information, 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 over 20 salaried internships in multiple research areas for summer 2022. The application deadline for these positions is on Monday the 31st of January 2022.

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 and statistics. 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.

Applying for an internship

To apply for an internship, use the following electronic form: https://elomake.helsinki.fi/lomakkeet/115582/lomake.html?rinnakkaislomake=CS_summer_jobs_2022.

Applicants must upload a study transcript (a list of passed exams and courses; a compulsory attachment) and, optionally, a one-page curriculum vitae and some other relevant dodument with the application form. All the attachments must be in a pdf format.

Important Dates

  • January 31st, 2022: Deadline for applications
  • February 1st – 28th, 2022: Possible interviews
  • During March 2022: Notification on decisions
  • In August 2022: Summer Interns' Seminar

Questions?

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.

The available summer internship positions in 2022 are the following:

Algorithms for DNA sequencing data

The genome of an oragnism can be investigated with DNA sequencing. DNA sequencing breaks the genome into small fragments and reports the nucleotide sequence of these fragments, i.e. substrings of the genome. We develop data structures and algorithms for analysing this kind of sequencing data. Possible topics for the summer internship include (i) lossy compression of sequencing data and (ii) indexing discriminating substrings of genomic data. The actual topic will be tailored according to the interests of the chosen applicant.

Programming skills and knowledge of algorithms and data structures is needed. Knowledge of biology or bioinformatics is beneficial but not necessary. The topics in this project are suitable for Master's thesis work.

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.

Automatic Deep Learning

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 troubles 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. 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 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 (multiple positions)

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) or data-efficient learning (domain adaptation, data-efficient reinforcement learning, data integration). You can also suggest your own topic that relates to our main research themes.

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 to have strong interest in pursuing doctoral studies. Both computer science and statistics students are encouraged to apply, and the topic is suitable as a MSc thesis.

Colored de Bruijn graphs

Colored de Bruijn graphs are a space-efficient model for reference sequence databases in bioinformatics. In this project, you will work on improving and extending our existing colored de Bruijn graph tool Themisto (https://github.com/algbio/themisto). This project is ideal for a student who is familiar with the C++ programming language and has an interest in algorithmic bioinformatics. The project can be extended to a master's thesis.

Constraint Reasoning and Optimization (multiple positions)

The Constraint Reasoning and Optimization Group has summer intern openings for summer 2022. Interns will engage in forefront research guided by senior researchers in the group. Topics include automated reasoning and optimization techniques for NP-hard real-world problems (ranging from theoretical analysis to practical algorithm development, implementation, and empirical studies); and symbolic techniques for formally verified and explainable AI. For successful internships, the work done during the summer may be extended into an 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.

GPU Computing for Big Data Processing

The main focus of the project is on the use of GPU computing to accelerate Big Data processing using the Apache Spark distributed computing framework. The job can be also alternatively be extended to a Master's thesis position on the topic. The summer job is in the context of the Academy of Finland projects "Design and Verification Methods for Massively Parallel Distributed Systems (DeVeMaPa)" and "Massively Parallel Algorithms and Analysis for Metagenomics and Pangenomics (MAPAMEPA)". In the project we will develop methodology for the design and verification of massively parallel heterogeneous computing. 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 programming paradigms infeasible and thus new methods need to be devised to cope with both Genomics and Industrial Big Data use cases.

Graph Algorithms: Engineering / Bioinformatics Applications (multiple positions)

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 graph algorithms, ranging from theory and experimental algorithmics to bioinformatics applications. See below our list of available projects, and mention in your motivation letter which projects interest you.

Implementation of deep learning models for complex-valued data (multiple positions)

Most of the present deep learning models deal with real-valued data. In some applications, for example in signal processing and wireless communication domains the use of complex-valued data provides better results. Therefore, there is a need to design deep learning models to process the complex data. Therefore, we are looking for 1-2 summer trainees to

1) contribute in research group’s work in implementing a complex Long Short Term Memory (LSTM) network,

2) collect data using the department’s satellite navigation signal generator (Global Navigation Satellite Signal GNSS Orolia Simulator) and by collecting the live GNSS signal outdoors,

3 process the data and compare the performance results between conventional LSTM and Complex LSTM for detecting the anomalies in GNSS signals.

Improving the Big Data Platforms MOOC Course

The task of the summer trainee is to help improve the home exercises and design an automated exam for the upcoming Autumn 2022 MOOC course on Big Data Platforms. The research group has already designed and run the course already twice and the main task of the trainee is to improve the used systems to allow for scalability to larger student volumes, create new home exercises, and to improve the scalability of the home exercises. The main requirements are good programming skills and interest in learning the latest Big Data Technologies with the help of the other members of the research group supervising the project.

Intelligent Algorithms (multiple positions)

The Sums of Products research group offers summer intern positions in 2022. We work on both theory and practice of exact and approximate algorithms for hard problems. The actual research tasks will be tailored for the intern based on their interests and skills, and may include research on the theory of algorithms, efficient implementation of algorithms, or heuristic algorithm designs evaluated mainly on empirically. Strong analytical skills are required. Examples of topics suitable for a Master's thesis are "Approximate counting of short paths: theory and practice" and "Selecting candidate parents for Bayesian learning of DAGs".

Machine Learning and AI for atmospheric transformations (multiple positions)

We have several topics in the fields of explainable AI (XAI), probabilistic AI, and building open-source tools.

(i) XAI for digital twins. We use machine learning algorithms together with physics simulators to model atmospheric transformations, measurement devices, and other processes. We call these models collectively "digital twins". In this project the task is to apply methods, developed, e.g., in our prior work to find useful and understandable explanations for the digital twins.

(ii) Uncertainty quantification for AI. In almost any real-world application of machine learning (atmospheric transformations included) concept drift is an issue, meaning that a model trained in some circumstances (e.g., under specific environment, for specific molecules etc.) may not work in other circumstances. The detection and quantification of concept drift is crucial: can we trust the outputs of our models?

(iii) Open-source tools for randomization and exploratory data analysis. Visual exploration of high-dimensional datasets and in the future of digital twins is a fundamental task in exploratory data analysis (EDA). We have developed a theoretical model for EDA, where patterns already identified and considered known by the user are input as knowledge to the exploration system. The user is shown views of the data where the user’s knowledge has been taken into account. In this project you will implement an open-source tool for exploratory data analysis. The tool should be web-based, cross-platform, and scale to large datasets. Programming skills and previous experience of open-source software development is considered an advantage.

Machine Learning for Ultrasound Physics

We are looking for an intern to work in the intersection of ultrasound physics and computer science, to develop machine learning methods for physical phenomena. We collaborate with physicists working at Department of Physics on two related topics:

(a) Propagation of ultrasound waves in metallic systems carries information about their geometry and possible defects/contaminations. We develop machine learning models (e.g., deep learning, Bayesian optimisation, Gaussian processes) for both supervised and unsupervised learning problems in this context, using signals recorded by multiple IoT sensing devices.

(b) Ultrasound waves can also be used to levitate objects and create acoustic holograms in the air. We develop optimization algorithms to determine controlling parameters for a specific task (contactless sample handling, tactile display). Most of the work can be done on simulations and the results can be verified on the physical device.

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.

Quantum computing programming for data management

Quantum computing is an emerging technology that harnesses the laws of quantum mechanics to solve problems. In this project, the candidate will gain experience in programming with Qiskit to solve some computation tasks related to data management.  The topic is suitable for a Master's thesis and we hope that the candidate can write a Master's thesis on this topic. Programming skills with Qiskit and knowledge of quantum algorithms are needed.

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.