PostDoc for Magnetic Resonance Research Center for Medical Applications

The quantitative MRI for Medical applications group (qMRI-MA) created on the base of the Department of Physics and Engineering at ITMO University is launching the project "Advanced Quantitative Technologies in Magnetic Resonance Imaging for Determining the Stages of Inflammation and Fibrosis as Disease Markers". The main goal of the project is to develop quantitative MRI methods and provide translational support for their use in clinical practice. One of the objectives of the project is to develop post-processing methods based on deep learning for obtaining quantitative metrics from MR images. The project will be led by Lead Scientist Dr. David Bendan, CNRS Research Director, Head of the SIMS Group (Spectroscopy and Imaging of the Musculoskeletal System) at the Center for Magnetic Resonance Imaging in Biology and Medicine (CRMBM) at the University of Ex- Marseille (Marseille, France).

Besides, at the Department of Physics and Engineering machine learning methods are now being introduced into current research projects, for example, for solving problems of inverse design of photonic structures, post-processing of NMR spectroscopy data, various optimization problems in optics, etc.

Within the framework of these projects, a post-doc position has been opened in the field of application of machine learning methods in physics and medical diagnostics.


  • scientific research conduction: the initial stage assumes the development of fully automatic methods of structures segmentation and classification in medical images based on existing machine learning methods;
  • participate in other research projects of the department where machine learning can be used for image processing and, besides, for processing experimental data of physical experiments, solving problems of structures optimization in radiophysics, photonics, optics;
  • planning and organizing work on projects;
  • advising colleagues on the possibility of solving their problems using machine learning methods;
  • participation in educational activities (conduct seminars on machine learning for students and colleagues);
  • taking an active part in the events and life of the department, the organization of summer schools and events for students and graduate students;
  • conducting scientific supervision of graduate and postgraduate students;
  • attract funding independently and jointly with colleagues (apply for grants, look for partners);
  • publishing articles;
  • speaking at conferences and seminars;
  • improving qualification both within the framework of courses and internships at ITMO University and beyond;


  • This position requires a Ph.D. in Physics and Mathematics / Engineering or a Ph.D. at least three years before applying for the position.
  • the topic of the Ph.D. thesis and the publication activity of the applicant should relate to the field of automated processing and analysis of images/data of a physical experiment using machine learning methods.
  • at least 2 years of experience in scientific projects/startups related to the application of machine learning in medicine/physics;
  • excellent programming skills are a basic requirement.
  • candidates with experience in both machine learning (including deep learning) and medical image analysis (with experience of using, for example, Python and related libraries (scikit-learn, TensorFlow, PyTorch, Keras, scikit-image), R, MATLAB, ITK-SNAP, Nilearn, FreeSurfer) are especially welcome.
  • experience in programming on video cards using CUDA and OpenCL.
  • experience of supervising students and / or graduate students.
  • experience of speaking at conferences.
  • recommendations from the group leader (where you defended the thesis or from the previous workplace).
  • knowledge of English at least B2 (Upper-Intermediate);
  • skill of information analysis and decision making;
  • initiative;
  • communication skills;
  • willingness for improvement.

An additional benefit will be:

  • availability of publications in high-ranking journals (IF> 3), including those with the first co-authorship;
  • knowledge of statistical methods as applied to the evaluation of the machine learning algorithms work;
  • availability of patents;
  • experience in applying and managing grants;
  • the presence of personal scholarships, awards, certificates of internship;
  • experience of teaching or scientific management of students;
  • work experience abroad.


  • young, friendly team;
  • communication with recognized experts in the subject area;
  • constant development, growth and step-by-step progress;
  • opportunity to suggest ideas and be heard;
  • corporate English for employees;
  • work in the historical center of St. Petersburg;
  • annual paid vacation;
  • social package: official employment, vacation in a corporate out-of-town health center, events for employees and their children, etc.

Lomonosova street, 9, Saint-Petersburg, Russia