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.
Requirements
- 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.
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TCS Lab - Theoretical Computer Science
Internship supervisor: Prof. Artem Maksaev
The Laboratory of Theoretical Computer Science was founded in December 2015. Structurally, it is a part of Big Data and Information Retrieval School at the Faculty of Computer Science.
The main research directions of the laboratory are computational complexity, algorithmic information theory, algorithmic statistics, combinatorial optimization, algorithmic aspects of game theory.
Topics: Computational complexity
Boolean circuit complexity, communication and information complexity
Algorithmic (Kolmogorov) complexity
Algorithmic statistics
Algorithm design and analysis
Algorithmic game theory
Combinatorial matrix theory
Graph theory, Random graphs and hypergraphs
General algebraic and number theoretic problems -
PAIS Lab - Process-Aware Information Systems
Internship supervisor: Prof. Irina Lomazova
Visual Representation of Distributed Systems
The key research directions in the PAIS Lab are mainly concentrated on formal methods and process mining. Formal methods comprise diverse methods to model and verify the behavior of processes occurring in distributed information systems using, for instance, finite automata, Petri nets and their extensions. Process mining focuses on discovering process models from the sequential records of their execution represented by event logs. Process mining aims to provide the AS-IS view on the processes in contrast to manually created models of idealized processes created at the early stages of the information system life-cycle.
Topics: Development and the experimental evaluation of the new approaches to modeling the behavior of complex distributed information systems using data recorded in event logs
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LAMBDA Lab - Methods for Big Data Analysis
Internship supervisor: Dr. Denis Derkach
The main scientific interests of the laboratory: the use and adaptation of machine learning methods to solve problems of natural sciences (and industry).
By subject areas:
1. Particle physics - partners JINR (Dubna) and NCFM (Sarov) and other expriments.
- fast selection of physical information;
- simulation of the response of the particle flight detector;
- search for the optimal design of detectors.2. Astrophysics
- solving the inverse problem.3. Material Science
- prediction of material properties;
- simulation of material behavior under external influence.4. Atmosphere:
- short-term weather forecast;
- weather simulator setup;
- problem of dispersion from pollution sources.From the point of view of machine learning:
- simulation by generative models - improving the quality of description, estimating the error of description, taking into account external information (laws of conservation or structure of the object);
- machine learning with structural constraints and information (for example, PINN);
- estimation of errors in regressions and generative models;
- optimization in black and gray box problems;
- acceleration of models;
- code generation.Topics: Use of LLMs for applied tasks (physics/industry)
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MMCP Lab - Models and Methods of Computational Pragmatics
Internship supervisor: Dr. Dmitry Ignatov
Multimodal Clustering for NLP, Knowledge Discovery in Databases and Recommender Systems
Aims and objectives of the Laboratory
We research and develop interpretable methods for processing user data, user action histories, and text data. We are creating new methods for processing texts in Russian that take into account such features of the Russian language as a rich morphological system and free word order in a sentence.
In addition, we consider it important to collect high-quality labeled cases - sources of language data - and publish them in the public domain, as The creation of new sources of language data is an important contribution to the development of the community and the state of the word processing industry as a whole.
We solve the following tasks:
Development and comparison of multimodal clustering and classification methods.
Development and comparison of methods of compression, rarefaction and regularization of neural networks, in particular, recurrent networks for language modeling.
Marking of cases of question-answer pairs, cases marked up by entities and events and the relationships between them.
Adaptation of standard architectures for English for the Russian language and development of own solutions for the tasks of finding the answer to a question, semantic parsing, information extraction, detection of quotes and indirect speech, etc.
Development of models for concise presentation (embeddings) of texts for recommendation systems.
Methods for extracting semantic patterns from texts based on data mining, analysis of formal concepts and others.
Understanding the intentions of users of recommendation and other, including mobile services.
The main activities of the Laboratory
Scientific and research activities: in accordance with the tasks listed above.
Educational activities: conducting courses on the subject of the Laboratory at the bachelor's and master's programs of the FCS, open seminars for the general public.
Project activity: implementation of industrial projects in cooperation with companies.Topics: Models and methods for text data analysis, recommender systems and data mining
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CSMC Lab - Complex Systems Modeling and Control
Internship supervisor: Prof. Viktor Popov
The laboratory pursues fundamental and applied research on data analysis and mathematical modeling of complex systems. The projects of the Laboratory are focused on the development of mathematical models and numerical methods for the reconstruction of the properties of big systems, which demonstrate synchronization phenomena, quasi-regularities, self-organization, and sudden regime changes. The reconstruction, based on the analysis of non-stationary time series, is performed to predict the dynamics of the underlying systems and create the mechanisms that allows us to control these systems efficiently.
Topics: Exploring Nonlinear Science in Non-Stationary Time Series and Mathematical Modeling for Complex System Control
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ATG Lab - Algebraic Transformation Groups
Warning: Pure Algebra
Internship supervisor: Prof. Ivan Arzhantsev
The theory of algebraic transformation groups, i.e., actions of algebraic groups on algebraic varieties,
is one of the classical areas of algebra and algebraic geometry. It has many interconnections with combinatorics,
differential geometry, algebraic group theory, Lie groups, Lie algebras, and representation theory.One of the main objects of our study is affine algebraic varieties. On the one hand, affine geometry studies local
properties of arbitrary algebraic varieties; on the other hand, it gives a geometric interpretation of natural questions
in commutative and differential algebra. The combination of algebraic, geometric, representation-theoretical and
combinatorial
methods allows us to use a wide range of tools in modern mathematics and obtain original results.The laboratory regularly organizes conferences, schools and seminars in affine algebraic geometry and transformation groups.
Within the laboratory, students have an opportunity to work on modern research projects, to publish their results in leading
mathematical journals and to take part in international collaborations.Topics: Automorphism groups of algebraic varieties, varieties with torus actions and graded algebras,
additive actions on complete varieties, locally nilpotent derivations, Cox rings and their applications -
Scientific and Educational Laboratory of Cloud and Mobile Technologies (CMT Lab)
Internship supervisor: Prof. Dmitry Alexandrov
The goal of the scientific and educational laboratory is to develop the IT direction related to the development of cloud and
mobile applications, including highly loaded distributed software, intelligent multi-agent systems and Internet of Things
systems, as well as systematization and generalization of the accumulated experience of the IT community in the field of
technologies for developing such systems. The laboratory regularly organizes seminars and summer schools in cloud and mobile development.Within the laboratory, students have an opportunity to work on research and applyed projects, and to publish their results.Topics: Research and Development of Methods for Creating Intelligent Multi-agent and IoT Software Systems
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International Laboratory for Intelligent Systems and Structural Analysis
Internship supervisor: Prof. Vasilii Gromov
Main tasks of the laboratory
Fundamental and applied research in the field of data analysis of large volume and complex structure (including complex structured data in textual, numeric, relational and structural forms).
Development of software tools for data analysis and creating of various components of intelligent systems.
Integration of the professional community in the field of data analysis and intelligent systems, facilitating the involvement of novice researchers.
Involvement of teachers, students and post-graduates of the National Research University Higher School of Economics in scientific work of the laboratory.
Adopting of research results of the laboratory in the educational process.
Preparation of new curricula and courses for "Applied Mathematics and Informatics" and "Data Science" majors at the NRU HSE.Topics: Building knowledge systems and data analysis based on textual information
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Centre of Deep Learning and Bayesian Methods
Internship supervisor: Prof. Aibek Alanov
The centre conducts research at the intersection of two actively developing areas of data analysis: deep learning and Bayesian methods of machine learning methods. Deep learning is a section that involves building very complex models (neural networks) to solve problems such as classifying images or music, transferring an art style from picture to photograph, predicting the next words in a text. Within the framework of the Bayesian approach, probabilistic models based on the apparatus of probability theory and mathematical statistics are considered for solving such problems.
Topics: Efficient parameterizations for generative models, including GANs and diffusion models; Novel approaches to Bayesian deep learning and uncertainty quantification; Speech enhancement and neural vocoding (e.g.,HiFi++, FFC-SE); Development of controllable generative AI systems; Research on acceleration methods for diffusion models
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.
Barycenters
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
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.
No, you do not need provide such certificate. However, your interview with a potential academic supervisor will be conducted in English.
No, there is not any, all internships are offered free of charge.
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.
Cecilia Tosciry (Oxford University)
In Moscow, I worked with two researchers: Professor Fеdor Ratnikov of HSE University, who works on the LHCb experiment at CERN, and Andrey Ustyuzhanin, who is responsible for joint projects of CERN and Yandex. At the beginning of the internship, Andrey Ustyuzhanin and I discussed my project in detail: he asked me about research problems and advised me on relevant articles. This was very useful: I learned about various algorithms for finding similarities between objects. But the trip to Moscow was remembered for more than just research. I was delighted with the local food - it was like going on holiday to a hospitable grandmother's house. All in all, the trip went well, and I even learned a little Russian.
Belhal Karimi (Ecole polytechnique (Université Paris-Saclay)
I really liked the organisation of the research, students and supervisors working on projects together. There were six or ten researchers in the lab every day, and we helped each other informally, sharing the results of experiments. Perhaps I will come to Moscow again: my supervisor at the Polytechnic School often goes to Russia. I enjoyed this trip - especially the Mayakovskaya district, where I lived, and the weekend I spent in St. Petersburg.
Leo Botelle (École Normale Supérieure de Paris)
I wanted to go to Russia for a long time, so I started reading about universities with strong data analysis programmes in Moscow. HSE University turned out to be one of them. At first, when I first arrived there, I was going to research applications of machine learning to build social graphs. The new theme was suggested by Sergey [Kuznetsov]: it turned out to be quite complicated and required a strong mathematical background. However, during the two months I spent in Moscow, I was able to sharpen my skills, which will be useful in my subsequent research.
Diego Granziol (Oxford University)
I cannot stress enough how lucky I was and what an honour it was to come to Russia, to work with the whole Bayesian Methods Research Group. The atmosphere was warm and welcoming. Timur [Garilov] is a truly amazing coder, and without him I would not have moved forward on any of my own ideas. I think he has the potential to do truly quite exceptional research, and I'm incredibly happy to follow his progress. I thank Dmitry [Vetrov], whose final contribution to the paper we sent to NeurIPS was absolutely essential, for our regular meetings, advice, questions, support and time.
Contacts
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