We are looking for ambitious students to join our group!

Students from universities in Moscow are welcome to join and do research with us in the form of projects, dissertations and individual studies.

Feel free to browse through the topics and contact us for further questions.

Theses Topics

Interested in doing research with us? We offer multiple Master theses, Guided Research projects and collaborations in the areas of data mining, machine learning and computer vision. A non-exhaustive list of open topics is listed below.

If you are interested in a thesis or a guided research project, please contact us with your CV and transcript of records via email. We will arrange a meeting to talk about the potential topics.

Sep, 2020 Statistical Models for Presurgical brain mapping based on resting-state fMRI
Sep, 2020 Probabilistic Inverse Graphics
Sep, 2020 Probabilistic Inference for MRI analysis
Sep, 2020 One-Class Classification for Anomaly Detection
Sep, 2020 Neuroeducation: Machine Learning for analysis of Brain development and education
Sep, 2020 Model selection for deep kernel density estimation
Sep, 2020 Learning with Expert Advice
Sep, 2020 Learnable Geometry Reconstruction via Deep Architectures
Sep, 2020 In search for an optimal deep network for MRI processing
Sep, 2020 Graph and Sequence Embeddings for Anomaly Detection
Sep, 2020 Global Registration of 3D Data in Robotics for Objects Localization Using Deep Learning
Sep, 2020 Geometric View On Deep Generative Networks
Sep, 2020 eSports athlete portrait and analysis via Machine Learning
Sep, 2020 Demand forecasting with Gaussian Process and Optimal Transport
Sep, 2020 Deep recurrent learning for dynamical flow data
Sep, 2020 Deep Neural Architecture Search
Sep, 2020 Deep Learning Representations of Unstructured Variable-Length Sequences
Sep, 2020 Deep Learning for Raster in the Wild: Vectorization Pipelines (with an application to floor plan vectorization)
Sep, 2020 Deep Learning for Range-Image Super-Resolution
Sep, 2020 Deep Learning and Network Compression for dense 3D pose estimation from 2D images
Sep, 2020 Deep Learning for MRI-based stroke outcome prediction and treatment planning
Sep, 2020 Deep Learning classification and segmentation for Localization of epileptogenic activity foci
Sep, 2020 Deep Graph Neural Networks
Sep, 2020 Deep Graph Networks and embeddings for classification and analysis of functional MRI
Sep, 2020 Deep Generative Modeling of Point Clouds
Sep, 2020 Deep Gaussian Process modelling for Machine Learning applications
Sep, 2020 Deep Convolutional Networks for fMRI-based Depression Classification
Sep, 2020 Data Reduction for Deep Networks
Sep, 2020 Causal time series co-clustering with Optimal Transport
Sep, 2020 Bayesian approach to Continual Learning of Generative Models
Sep, 2020 Bayesian approach to Continual Learning of Generative Models
Sep, 2020 3D Deep Learning
Mar, 2018 Survey of bayesian non-parametric regression methods
Mar, 2018 Survey & application of regression methods exploiting graph structures
Mar, 2018 Methods for multi-output regression
Mar, 2018 Survey & development of new methods for dynamic hierarchical time series models
Mar, 2018 Survey of regression methods for large data sets
Mar, 2018 Bayesian Time Series


Interested in working with us?

We are looking for talented and highly motivated computer scientists (or people with a related background) interested in the design, development, and analysis of novel machine learning methods. Particularly, we are currently offering positions focusing on the following topics:

  • Machine learning for graphs / geometric deep learning
  • Robust and adversarial machine learning
  • Transfer learning
  • ML models for temporal data (e.g. event sequences)
  • Anomaly and outlier detection
  • Uncertainty in ML (e.g. Bayesian neural nets)

The developed methods will be applied and evaluated in various domains such as the natural sciences (e.g., molecular graphs), the field of engineering (sensor and diagnosis signals), and the web (e.g., social networks, knowledge graphs).

Candidate skills & profile

  • University degree (M.Sc.) with very good grades in Computer Science or related fields (For PostDocs: Ph.D. in the corresponding area and publications at the following venues: ICML, KDD, NeurIPS, ICLR, or WWW)
  • Strong background in machine learning / data mining
  • Strong programming skills in at least one programming language (preferably Python and with experience in TensorFlow, PyTorch or similar)
  • Good English language skills (your responsibilities include to write publications and to give international presentations)
  • Knowledge of Russian is an asset, but not a must (e.g. participation in national conferences)

How to apply?

Please contact us and send your application (in a single file in pdf format; no links to external files; in English or Russian) by email; subject: PhD Application). The application should include:

  • a brief statement of interests in the form of a motivation letter,
  • a curriculum vitae, copies of certificates,
  • a summary/abstract of the master thesis,
  • and (if already available) a list of publications.

A list of references (names, contact information) is helpful as well.

Applications will be considered as they are received and until the positions are filled.