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Postdoctoral Fellow in Machine Learning and Statistical Physics

Laboratory for Computational Physics Moscow, Russia

Laboratory for Computational Physics in Moscow, Russia, invites applications for postdoctoral research positions in the field of application of machine learning in statistical physics, with the primary interest in phase transitions and critical phenomena.

Requirements

The general requirements for the postdoctoral fellowship positions are the following:

●     Candidates must hold a recent PhD in one of the field of computational physics, machine learning, phase transitions, statistical physics, applied mathematics or related areas which was awarded over the last 5 years or received before starting work at HSE in a relevant field by an internationally recognized university and has been assessed by external reviewers as having the potential to pursue research that is publishable in leading peer-reviewed journals;

●     Candidates should have a strong background in computational techniques, Markov chain Monte Carlo, Probability Theory, statistical physics, ability to work in team and conduct research in specific scientific programs.

●      Fluent English is an obligatory condition as research and other activities are conducted in English. Knowledge of Russian is not required;

●     Relevant experience will be an asset although not required.

The position involves:

●     working under the direct supervision of professor Lev Shchur ;

●     participants are encouraged to pursue their own research along with working on Laboratory of Computational Physics research projects such as:

o  Methods for extracting properties of statistical mechanics models from deep machine learning

o  Event-chain molecular dynamics

o  Parallel discrete event simulations

●     writing research papers for international peer-reviewed journals in co-authorship with the members of the Laboratory of Computational Physics;

●     participation in the events of the Laboratory of Computational Physics and other contribution to the Laboratory of Computational Physics development;

●     public presentations of candidate’s own research to the academic community;

●     some teaching is encouraged, though not required.

Conditions

Appointments are made for one year. Postdoctoral fellows have high opportunity of renewal of the contract (no more than two times) in case of outstanding performance.

HSE University offers postdoctoral fellows a competitive salary, the standard medical insurance plan; a working space equipped with a computer and free Internet access at the University.

The Laboratory of Computational Physics offers access to its high-performance computational facilities, digital libraries, etc.

Laboratory for Computational Physics in Moscow, Russia, invites applications for postdoctoral research positions in the field of application of machine learning in statistical physics, with the primary interest in phase transitions and critical phenomena.

Application Process

Applications must be submitted online. Please provide a CV, a statement of research interest and a recent research paper submitted via the online application form. At least two letters of recommendation should be sent directly to the International Faculty Recruitment Office at fellowship@hse.ru before the application deadline. Please note that direct applications to the hiringLaboratory of Computational Physics may not be reviewed.

Read more about the application process here.

 

The deadline for the applications is February 8, 2024

 

Apply now

For more information:

-       about Postdoctoral Fellowship -Frequently Asked Questions

-       about HSE university –official web-site

-       about the Laboratory of Computational Physics– contact Professor Lev Shchurlshchur@hse.ru directly, head of the Laboratory of Computational Physics

-       about international specialists' life in Moscow -International Faculty Support page

 

If you have any additional questions, feel free to contact the International Faculty Recruitment Office at fellowship@hse.ru


 

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