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Natural & Applied Sciences
Applied Bioinformatics
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Applied Bioinformatics

Moscow Institute of Physics and Technology
On campus
 1232 Points
2 Years

Program Description

This research and training program aims to provide students necessary skills and knowledge for using the large-scale biological data obtained in the course of biological experiments, including various “omics”, epidemiological and pharmacological studies. Applied bioinformatics also uses methods based on statistics and machine learning. Students gain in-depth knowledge in the field of modern molecular biology, genetics and related biological disciplines.

Industrial Bionformatics is a promising program for the further employment both in science and in many commercial enterprises. We invite students with basic knowledge in maths and computer science to perform calculations in bio. This program is built by experts, who proved themselves both in industry and academia.

Our educational program sends students to leading international and Russian research centers and institutes, some of which participated in the creation of the program, such as: Georgia Tech, BostonGene, Martin Luther universität halle-wittenberg, Sechenov university, Phillips etc. During their studies, our students are involved in research work with our key partner BostonGene. For students who have not fully mastered any of subjects, individual trajectory of study will be created, including the Coursera and Stepik platforms.

Entry Requirements

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Semester 1

Foreign language

History, philosophy and methodology of science

Introduction to Molecular Biology and Genetics

Machine learning

General scientific disciplines (at the choice of the student)

Theory of Probability and Mathematical Statistics

Numerical and optimization methods

Protein Physics and Structural Bioinformatics

Programming Languages (workshop)

Structures and databases


Molecular modeling

Perform research in the laboratory at the choice of the student

Semester 2

Foreign language

Basic Biostatistics

Microbiology and metagenomics

Algorithms of bioinformatics

History, philosophy and methodology of science

Data Visualization (Workshop)

NGS Data Analysis

Population genetics

Medical genomics

Deep learning

Transcriptome, epigenomics and analysis of OMIKS data

Analysis of OMIKS data (workshop)

Professional disciplines (at the choice of the student)

Parallel programming technology

Perform research in the laboratory at the choice of the student

Semester 3

Perform research in the laboratory at the choice of the student

Semester 4

Undergraduate practice


  • Machine Learning
  • Research Engineer
  • Research Scientist
  • Junior/Senior bioinformatics specialist

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