PROJECT NAME
ANALYSIS OF BIG EXPERIMENTAL DATA BASED ON SUPERCOMPUTER MODELING AND MACHINE LEARNING TECHNOLOGIES
The goal
The goal of the research and development project is to develop a comprehensive non-destructive in-situ and operando nanometrological X-ray spectral characterization of the parameters of the local atomic and electronic structures of materials using modern instrumental methods, including the latest generation of synchrotron radiation sources
The language of researchers – Russian & English
  • Methods:
    − Modern experimental methods for characterization of materials (XANES, EXAFS, XAFS pre-edge) including studies at synchrotron radiation facilities;
    − Theoretical modeling using DFT and molecular dynamics methods;
    − Machine learning algorithms for analysis of experimental (big) data;
    − Theoretical quantum chemical modeling in materials science;
    − Machine learning for big data analysis and instrument control.
  • Equipment:
    − Supercomputer cluster, including specialized nodes for calculations using artificial intelligence technologies;
    − Synchrotron radiation facilities;
    − The only laboratory spectrometer R-XAS (Rigaku) in Europe.
Head of research project
  • Alexander Soldatov
    Doctor of Physical and Mathematical Sciences, Scientific Director «Materials Science and Synchrotron-Neutron Research» of Southern Federal University, Acting Director of the Smart Materials Research Institute SFedU, Head of the scientific laboratory «Frontier Laboratory of X-ray Spectral Nanometrology».
    Personal page
    Experience of scientific and pedagogical work – 44 years
    Research interests: Theoretical and experimental study of local atomic and electronic structure in various types of advanced nanomaterials, including: quantum dots, nanocatalysts, metal-ion battery materials, biomedical nanomaterials and others.

    Under the supervision of professor Soldatov, 29 applicants received PhD degree, and 5 received Doctor of Science degree. At the moment, there are 7 applicants for PhD degree under his supervision, and he is a consultant for 3 applicants for Doctor of Science degree.

    He is actively involved in teaching. He has written the textbook «Solid State Physics», over 57 teaching aids and three electronic multimedia textbooks.

    Professor Soldatov has developed and implemented the following courses: «Solid State Physics», «X-ray Spectroscopy», «Nanoscale Structure of Matter», «Electronic Structure of Matter», «Physics of Nanoclusters», «Multiscale Computer Modeling».

    He has worked as a visiting professor at leading foreign educational centers: Sapienza University of Rome (Italy), The University of Western Ontario (Canada), University of Antwerp (Belgium), University of Nijmegen (Netherlands), Free University of Berlin (Germany). He is a member of the editorial boards of a number of scientific journals, including the Journal of Structural Chemistry of the Russian Academy of Sciences, Journal of Surface Investigation: X-Ray, Synchrotron and Neutron Techniques, Nanotechnologies in Russia.

    ORCID: 0000-0001-8411-0546
    WOS Research ID: E-9323-2012
    Scopus Author ID: 7102129914
    ID RSCI: 1685
    more information
POSTDOC
Place of work – Smart Materials Research Institute of Southern Federal University
The field of science and scientific directions – Nanotechnology, Materials, Physics
The estimated duration of work is 2-3 years.
  • Goals:
    The postdoc will be involved in cooperation with a team of specialists in the field of chemistry, physics and computer modeling. The postdoc must form a team around him/herself that will be able to solve diagnostic problems in the process of synthesizing new materials using machine learning methods.
  • Postdoc job requirements:
    − age up to 39;
    − PhD degree (or equivalent) in materials science, applied mathematics, chemistry, physics;
    − fluent English (at least upper intermediate level - B2);
    − at least 3 publications (Scopus and Web of Science) on the topic of the project;
    − experience in data analysis and machine learning, with a focus on applications in spectroscopy, cheminformatics and materials science;
    − general programming skills related to machine learning (preferably Python with the ability to use tools: Pandas, numpy, sklearn, scipy, statmodels, pytorch\jax\tensorflow);
    − experience in writing scientific articles and literature reviews.
  • Expected results:
    publication preparation, RIA preparation, student supervision, training of scientific personnel, participation in scientific conferences
Salary – 120,000 rubles.
Additional support –support in obtaining a visa, official housing, assistance in employment and education of family members, internships, trips, visits to medical institutions, Russian language courses, etc.