PhD Positions in Machine Learning and Artificial Intelligence


We are currently inviting applications by for PhD positions in Machine Learning and Artificial Intelligence, starting in November 2017.

The successful applicant is expected to be interested in machine learning (ML) and artificial intelligence (AI), and especially in deep learning (DL) and/or tensor decomposition (TD).

Successful candidates will be expected to complete their PhD thesis and to participate in Tensor Networks and Deep Learning laboratory, as well as to acquire the skills specific to DL/TD project and available within Skoltech.


  • Successful candidates are expected to have a Masters (or Specialist or an equivalent) degree in a relevant area.
  • Skills in Programing: Python, C++.
  • Basic knowledge of linear algebra, artificial neural networks and deep learning.
  • The candidates should have at least an upper-intermediate level of the English language.


Application Procedure

Prior to submitting an application, students are advised to contact Professor Andrzey Cichocki ( and/or Professor Ivan Oseledets (

Please submit a PhD application including:

  • a motivation letter (statement of purpose),
  • a resume/CV, with the publications list (if any),
  • 2 letters of recommendation, and
  • proof of proficiency in English, i.e., TOEFL or IELTS (students may submit an application without this information, but all will be asked to provide it at a later date)

through Skoltech Admissions for Doctoral Programs, selecting “PhD” as the degree level, indicating the name of the prospective supervisor, and choosing “CDISE” from the list of centers.

All documents should be submitted in English (the exceptions are diplomas in Russian and publications in Russian).


Further information

Information about Skoltech:

Information about CDISE:

Queries regarding research interests:   Professor Andrzey Cichocki (, Professor Ivan Oseledets (

Queries regarding application process:


Skoltech is committed to diversity and opportunity, and application materials will be considered without regard to the gender, race, or nationality of the applicant.