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Regular version of the site

The Faculty of Computer Science invites international students to participate in research internships. The duration of the internship is two to six months.

About the Faculty

The Faculty of Computer Science has sixteen laboratories engaged in research in theoretical computer science, big data, optimization methods, machine learning, computer vision, software engineering, bioinformatics, and topology. The Faculty cooperates with the most importamt Russian and internations research institutions, including the Russian Academy of Sciences, CERN, Samsung, and Yandex. Scientists from all over the world take part in the work of the laboratories and teaching at the Faculty. Regular conferences, schools, laboratory seminars, and the Colloquium of the Faculty are held.


  • Being an undergraduate, graduate, or postgraduate student
  • Experience in the research area of the chosen internship
  • Good command of English (Russian is not required)

Application procedure

Your application should include:

  • CV
  • Cover letter
  • University transcript with your grades
  • The name of the project you’re interested in
  • English proficiency certificate (not obligatory)

On successful completion of the initial selection, the candidate is invited for an interview with a potential academic supervisor. The interview is conducted in English.

Important notice

Please note that the review process may take a few weeks. We will get back to you as soon as we have made our decision, whether positive or negative. No application will remain unanswered!

You can apply for an online or offline internship or consult us.


  • Solving combinatorial games

    Studying Sprague-Grundy and and Smith functions of combinatorial games hypergraph NIM and slow NIM. Constructing efficient algorithms solving these games.

    Prerequisites:  Good skills in combinatorial proofs, a rather good experience in solving combinatorial problems by computations

  • Visual Representation of Distributed Systems

    In this project, your goal will be to prepare a research report with comparative analysis of existing methods to visualize distributed systems. Both structural (static) and behavioral (dynamic) aspects are important. Good reading and writing skills are needed. Besides, it is expected that you not just find and mention but try to reproduce considered methods of visual representation. Well-made project report can be a starting point for further research collaboration, for example, on the base of HSE University PhD School.

    2D Graph Drawing Methods

    In this project, your goal will be to study and survey various graph drawing methods. We are interested more in so called layered layouts (Sugiyama-style algorithms etc.), but other human-readable layouts can be considered as well. You will have to define some comparison criteria (including theoretical and practical algorithmic complexity) and compare graph layout algorithms according to them. So, a good knowledge of algorithms is needed. The output of this project will be a research report that can lay a foundation for further research collaboration, for example, on the base of HSE University PhD School.

  • Tensor decompositions and applications

    Tensor decompositions naturally generalize the concept of singular value decompostion (SVD) to the case of multidimensional arrays (tensors). In this project you will work on developing tensor approximation algorithms based on modern techniques such as Riemannian optimization or randomized algorithms. Possible application of the algorithms include compression of neural networks and building recommender systems.

    Improving robustness of neural networks

    Large neural networks can often be affected by instabilities of training or be sensitive to specifically constructed perturbations of data (adversarial attacks). It appears that the arsing instabilities heavily depend on spectral properties of neural network building blocks. The project will be devoted to developing algorithms for improving these spectral properties using numerical linear algebra techniques.

  • NL2ML (Natural language to machine learning)
  • Multimodal Clustering for NLP, Knowledge Discovery in Databases and Recommender Systems
    Machine Learning on Graphs (including graph embeddings)
    Benchmarking Deep Learning and Neural Architecture Search for NLP
    Sparsification and Compression of Deep Learning models
    Interpretable and Fair Machine Learning and Recommender Systems
    Combinatorial Search and Optimization with Formal Concept Analysis
    Boolean Matrix Factorization and its Applications (including RecSys)
    Sequence, Stream and Graph Mining and their applications
  • Internship supervisor: Dr. Muhammad Shahid Iqbal Malik

    Cross-lingual and Multi-lingual Text Classification and its Explainability for Abusive Content Detection from Social Media
    Neural NLP learning: Exploration of multi-task learning, transfer learning, and active learning approaches for Social Media Mining
  • Data analysis and modelling of processes in the Sun (in the stars), in the solar (stellar) wind, in the magnetospheres of planets (exoplanets)
  • Theory of n-valued groups, hyperbolic geometry and applications
    Theory of branched coverings
    Group actions on manifolds, algebraic topology and combinatorics of orbit spaces
    Bigraded persistent double homology and torus actions
    Hyperbolic manifolds, reflection groups and cohomological rigidity
  • ATG Lab - Algebraic Transformation Groups

    Warning:  Pure Algebra

    Internship supervisor: Prof. Ivan Arzhantsev

    Locally nilpotent derivations and automorphisms of algebraic varieties

    We study automorphism groups of both affine and complete complex algebraic varieties applying algebraic and combinatorial methods. Main tools include graded algebras, locally nilpotent derivations, Cox rings and Demazure roots of toric varieties.

    Prerequisites: Basic knowledge of Commutative Algebra, Algebraic Geometry and Representation Theory


Ask your question

Process Simulation and Log Generation

Gena is a simple tool that allows generating event logs by executing process models. It is useful in the testing and evaluation of process mining algorithms. Gena supports two important notations used in business process modelling and process mining: Petri nets and basic BPMN models. However, many improvements can be made in Gena to support more sophisticated simulation scenarios. Interns have an opportunity to join this project.

Closures and lattices for data analysis and knowledge discovery

We study applications of interpretable (explainable) tools of lattice and order theory, including closed descriptions, minimal generators (keys) in projects on data analysis and knowledge discovery.

Geometry of J

The first goal of this project is to further understand the geometric properties of this metric. For instance, one objective would be to study whether the set P(R^d), endowed with J, is a geodesic metric space, characterize its shortest paths and eventually its curvature properties. Detailed description.


A related field of investigation is to study barycenters relative to metric J. One natural question is whether such barycenters exist, are unique and if one can estimate them in a consistent way. These questions could be studied in the light of the recent paper by Le Gouic and Loubes (2017). Detailed description.

Variance inequalities

Given the recent work by Ahidar-Coutrix et al. (2018), another interesting question is whether one can establish so-called variance inequalities for the metric J. Such inequality was shown to guarantee fast convergence rates for certain estimators of barycenters in Ahidar-Coutrix et al. (2018) and would be of great interest from a statistical point of view. Detailed description.

Data analysis and modelling of processes in the Sun (in the stars), in the solar (stellar) wind, in the magnetospheres of planets (exoplanets)

Data analysis, monitoring, modelling and forecasting of space weather.

About HSE University

Consistently ranked as one of Russia’s top universities, HSE University is a leader in Russian education and one of the preeminent universities in eastern Europe and Eurasia. Having rapidly grown into a well-renowned research university over two decades, HSE University sets itself apart with its international presence and cooperation.

In March 2014 HSE University together with Yandex, a major Russian IT company, opened its new Faculty of Computer Science. The Faculty aims at preparing highly qualified data scientists, software engineers, and computer science researchers for leading Russian and international IT companies and academic institutions.

Frequently asked questions

Who can participate?

We invite current undergraduate, graduate, and postgraduate students from all over the world. The key requirement is experience in the research area of the internship.

Do we need to provide an official English language certificate?

No, you do not need provide such certificate. However, your interview with a potential academic supervisor will be conducted in English.

Is there a registration fee?

No, there is not any, all internships are offered free of charge.

Is there any scholarship or financial support?

You can apply for financial aid from your university.

Past participants

Students from Oxford University (UK), École Normale Supérieure de Paris (France), Università degli Studi di Padova (Italy), Université de Toulouse (France), Instituto Superior Técnico (Portugal), École Centrale Supérieure de Marseille (France), INSA Lyon (France) have participated in our internships.


Vera Shinakova

Отдел внешних коммуникаций: Менеджер