The chair is lead by Prof. Filippo Vicentini in a recently created research group with 2 other PhD students and some interns. Filippo Vicentini is recognized as one of the most prominent figures in the nascent field of Neural-Network quantum states, having contributed to the development of several fundamental ideas to describe systems out-of-equilibrium, as well as several algorithms for simulating their dynamics. F.V. also leads the NetKet open-source collaboration, developing the most widely used software in the field. The group is supported by CNRS, has been awarded an ANR Junior Researcher grant and a JRC startup fund. The group is also affiliated to INRIA Saclay and College de France and regularly visits and partecipates in joint activities with those institutions. The group maintains strong ties with the group of Prof. Giuseppe Carleo in EPFL, and other research groups overseas. 

The objective of the group is to expand the boundaries of what can be simulated with Classical and Quantum algorithms, leveraging ideas from optimisation theory and Machine Learning, and to apply such algorithms to answer open problems in fundamental quantum science (Entanglement Phase Transitions, Transport problems...) as well as in condensed matter physics (correlated electrons in solid state and chemistry). Core interests of the group at this stage are (i) methodological improvements to simulate the quantum dynamics and/or accessing the spectra of correlated systems, (ii) methodological improvements to the simulation of systems out of equilibrium, (iii) application of said methods to correlated electrons, (iv) foundational understanding of neural quantum states and their resource theory/representability arguments and (v) connections to quantum computing and hybrid classical-quantum algorithms. 

 Candidates are expected to have (or be awaiting its award) in the area of Theoretical/Computational Physics and a strong expertise in numerical methods for one of correlated electrons, systems driven far from equilibrium or closely related problems. Experience with Machine Learning is not strictly required but is given significant weight. Candidates must show a clear, genuine interest and plausible research plan to combine their interests with the research of the group. Experience with Influence Functional, Variational Monte Carlo or Quantum Algorithms will also be evaluated positively. Experience supervising interns/younger students will also be evaluated positively. 

Succesful candidates will be expected to work on some of the topics mentioned above, but will also be encouraged and supported to combine their previous experience in this new setting. An ideal candidate would also express an interest in taking on co-supervising master interns and collaborating with PhD students. The group is strongly engaged in the open-source philosophy, and postdocs are expected to take on a role curating and contributing algorithms to the larger scientific community. 

Preferred starting date are either 1st of January or 1st of April, but later dates can be negotiated. International and female applicants are encouraged to apply. The contracts are for a 1 year renewable position, with funding currently available for up to 3 years each. Candidates will be reviewed on a rolling basis and the position will remain open until filled. 

For further informations, please contact Filippo Vicentini at filippo.vicentini[at]polytechnique.edu . To get an idea of the research direction of the group, you can look at recent papers Google Scholar 

If you are interested, apply with: 

 - A cover letter/statement of interest mentioning what particular research lines of the group they would be interested in contributing to (max 1/1.5 pages); 

 - A research statement of 1 to 3 pages discussing past work, proposing possible avenues for research and potential sinergies with the research intersts of the group; As a component of their statement, applicants are encouraged to also discuss how they expect to bring to the group and what they expect to get from this experience. 

 - Curriculum Vitae (CV) with bibliography (and expected graduation date if relevant); 

 - 2 to 3 reference letters.