The Skolkovo Institute of Science and Technology (Skoltech) is a private research institute in Moscow, Russia.
Established in 2011 in collaboration with MIT, Skoltech cultivates a new generation of researchers and entrepreneurs, promotes advanced scientific knowledge and fosters innovative technology to address critical issues facing Russia and the world. Research is conducted in ten main areas: life sciences, neurobiology and brain restoration, computational and data-intensive science, energy science and technology, hydrocarbon recovery, design, manufacturing and materials, space, advanced studies, and photonics and quantum meterials. Go to the Skoltech website to learn more.
Featured internships for summer 2020:
#1. Data processing and visualization in Virtual Reality (DataVR)
Data science is already considered the fourth pillar of science together with experiments, theory and computational science. One can gain a lot of insight and knowledge from handling and processing large amounts of data, the volume of which is growing exponentially. Data visualization (bringing processed data to the form easily understandable by humans) is of critical importance. There are a lot of frameworks for 3D data visualization, to name a few: ParaView, Visit, Blender. The aim of this challenging and interesting summer project is to create, test and present new frameworks for data visualization in virtual reality (VR) using APIs of different VR devices. Candidates for the internship in DataVR should have some experience in 3D data visualization (OpenGL), knowledge of Python, and preferably C/C++ and Blender.
#2. Internships at the Intelligent Space Robotics Laboratory (ISR)
1) Autonomous navigation, mobile robotics, SLAM, and scan-matching. 2) Swarm of drones, and quadcopter flight control. 3) Smart robotic factory with embedded computer vision (joint project with MIT Professor Kamal Youcef-Toumi) 4) Haptics, tactile displays for Virtual Reality.
#3. Internships in the Deep Quantum Laboratory
MIT students are invited to spend 3 months or longer conducting research in the areas of quantum theory, and quantum technology at Skoltech University’s Deep Quantum Laboratory, which focuses on theory and quantum algorithms development and has a long history of collaboration with MIT. Those admitted into the program will join the international lab led by Prof Jacob Biamonte. The lab has successfully hosted students through the program before and has ongoing projects related to the theory of tensor networks, machine learning applied to quantum physics, using quantum computers to accelerate machine learning tasks, quantum walks on complex networks etc.
#4. Search for novel materials and unusual chemical compounds using artificial intelligence.
#5. Internship in computer vision or medical vision.
#6. Internships at the Center of Entrepreneurship and Innovations. Possible projects: a) developing and teaching workshops on business and entrepreneurship, b) working with Skoltech’s Startup Club and helping them with their ongoing projects.
#7. Development of digital platform for virtual point mutation screening using machine learning and atomic structures of membrane proteins
Point mutation is the minimal change in the genome, that, however, can lead to dramatic consequences, as for example disease and pathology. Machine learning and data analysis of large-scale point mutation screening allow to derive prediction models for the personalized medicine, as well as for design of proteins with desired properties. Recently, we developed computational approach [1,2] to design thermostabilizing mutations in G protein-coupled receptors, one of the most important pharmacological protein family. With this method, it became possible to resolve three-dimensional structures of several pharmacological targets [3,4,5,6,7,8]. The project of this internship is to develop digital platform for virtual point mutation screening using machine learning and atomic structures of membrane proteins.
Data types: sequences (1D), sequence alignments (2D), protein structures (3D), and its high-dimensional representations (tensors, R^n)