Q@TN is a joint initiative of the University of Trento, the Bruno Kessler Foundation, and the National Research Council aimed at coordinating their present and future activities in the field of Quantum Science and Technologies. Q@TN promotes research projects, technological transfer, education and training.
The project “ARTificial Intelligence for Quantum Systems” (ARTIQS) is inherently interdisciplinary, fostering cross-fertilization between the machine-learning and network-theory communities in ICT, the computational physics community and materials scientists in Trento, with a unique opportunity for the post-doctoral associate to work within and beyond the realm of quantum physics.
In particular, a tight collaboration will be sought between the following units (alphabetic order):
FBK invites applications for a research position in the field of machine learning methods for quantum physics, for the development of a deep-learning based representation of quantum statistical systems in large Hilbert spaces, with particular emphasis on many-body and condensed-matter physics.
The ideal candidate has recognized scientific experience at international level and significant expertise in at least one of the two main topics of the ARTIQS project: quantum many-body physics and deep-learning methods. A plus will be experience in modelling and simulation of quantum systems and experience with (quantum) networks.
The candidate is expected to work in team, help developing innovative research and contribute to scientific dissemination through journal papers and conference talks.
The ideal candidate should have:
- Master in Physics or Engineering;
- Proven track record of independent research;
- Good knowledge of written and spoken English;
- Propension to work in team, problem solving attitude and flexibility.
- Ph.D. in Physics, Mathematics, Computer Science or Engineering (candidates in the final stage of their Ph.D. might be considered, provided that they have been at least admitted to the final defense of their thesis by the starting date of the project);
- Previous experience in modeling and analysis of quantum many-body systems;
- Previous experience in machine learning (better if applied to physics problems);
- Research experience within an international environment;
- Programming skills in high-level programming languages for High Performance Computing (C++, C, FORTRAN);
- Programming skills in state-of-the-art deep-learning frameworks (Pytorch, Keras, Tensorflow).
Note that priority will be given to senior candidates with experience in the research topics relevant for the project.
Type of contract: fixed term contract
Working hours: full time
Gross annual salary: from 34.300 € to € 39.300, depending on the profile of the successful candidate
Start date: First Quarter 2019
End date: December 2021
Place: Povo, Trento (Italy)
Benefits: flexi-time, company subsidized cafeteria or meal vouchers, internal car park, welcome office support for visa formalities, accommodation, social security, etc., reductions on bank account opening fees, public transportation, sport, language course fees. More info at https://www.welfarefbk.info/
Candidates are required to submit their applications by clicking "Apply online" at the bottom of this page.
Please make sure that your application includes the following attachments (.pdf format):
- detailed CV and list of relevant scientific publications,
- letter of motivation,
- at least two professional references (e-mails and/or phone numbers).
Application deadline: 22nd January, 2019
Please read our Regulations on the recruitment and selection of fixed-term personnel (effective from October 15, 2018) before completing your application.
For further information, please contact the Human Resources Services at email@example.com