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We welcome applications by Postdoc candidates at the interface ofMachine Learning for statistical Physics at FU Berlin. Strong theoretical and computational background are required. Example projects include Boltzmann Generatorshttps://arxiv.org/abs/1812.01729Learning coarse-grained Molecular Dynamics force fieldshttps://arxiv.org/abs/1812.01736Please send applications with CV, research interests and references toFrank Noe, frank.noe@fu-berlin.de

The department of Applied Physics at Eindhoven University of Technology, The Netherlands, is inviting applications for 9 new faculty positions. The positions are open at all levels, i.e. Assistant, Associate, and Full Professor, and we are looking for candidates in all fields related to Applied Physics, including statistical physics, soft matter, biophysics, and fluid mechanics.

Dear colleagues, The department of Applied Physics at Eindhoven University of Technology, The Netherlands, is inviting applications for nine (9) new faculty positions. The positions are open at all levels, i.e. Assistant, Associate, and Full Professor, and we are looking for candidates in all fields related to Applied Physics, including soft matter, biophysics, and fluid

——– Forwarded Message ——– Subject: [soft-matter] postdoctoral position in theoretical-computational membrane biophysics Date: Wed, 22 May 2019 11:48:00 +0000 From: Oded Farago To: ‘soft-matter@lists.leeds.ac.uk’ A postdoctoral position in the area of membrane biophysics is available in my research group at Ben Gurion University, Israel. Our group applies analytical and computational methods of statistical mechanics to

Postdoc position in Statistical Inference & Learning at ICTP, Trieste, Italy, starting summer/fall 2019.Candidates should have a background in mathematics (probability, statistics), theoretical physics (statistical physics), computer science, information theory, electrical engineering and/or related disciplines, and be able to carry out active, independent and multidisciplinary research in the following areas: Statistical Inference & Learning: Mathematical