Welcome to CDISE!

Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) aims at the conduction of cross-cutting interdisciplinary research driven by modern applications in the fields of computational and data sciences. CDISE has accumulated several research groups with the expertise unique in Russia and competitive at the world level in their prospective areas. Currently, there are 17 professors, more than 50 researchers, and 60 Ph.D. students. CDISE powers the largest educational program at Skoltech – the IT program with two tracks: Computational Science and Engineering and Data Science. The research areas include but are not limited to:

• Machine learning and Deep Learning
• Data Management and Data Mining
• Next generation multiscale modeling
• Fast solvers for large scale / high-dimensional problems
• Efficient material design and quantum chemistry computations

The Center is actively involved in collaboration with other Skoltech CREIs to provide expertise on data-intensive computational tasks.

Vacancies

Currently we invite applications for the following faculty positions:

• Associate Professor in Computer Vision
• Associate Professor in Advanced Computing
• Assistant Professor in Reinforcement Learning
• Assistant Professor in Data-Driven Simulations
• Assistant Professor in Inverse Problems and their applications

We also have a number of other vacancies in the fields of Mathematics, Statistics, and Machine learning. For more details, please refer to the Vacancies page.

Research groups

• Advanced Data Analytics in Science and Engineering, Prof. Evgeny Burnaev
• AeroNet Lab
• Applied Information Theory Group
• Computational Imaging, Prof. Stamatios Lefkimmiatis
• Computational Molecular Science, Prof. Maxim Fedorov
• Computer Vision, Prof. Victor Lempitsky
• Internet Of Things Lab, Prof. Dmitry Lakontsev
• Laboratory “Tensor networks and deep learning for application in data mining”, Prof. Andrzej Cichocki
• Mobile Robotics Lab, Prof. Gonzalo Ferrer
• Scientific Computing, Prof. Ivan Oseledets
• Structural Learning Group, Prof. Vladimir Spokoiny

 

You can find out more about research groups.