Candidates should have a Ph.D. in Chemistry, Physics, or related fields. A strong background in computational/theoretical chemistry/physics (e.g., statistical mechanics, computer simulation, electronic structure theory) and programming skills are essential. The main theme of the project is the development and application of machine learning-based energy functions (force fields) for atomistic biomolecular and chemical simulations.
The research will use a combination of in-house developed molecular simulation and machine learning methods to characterize the conformational dynamics and spectroscopy of condensed-phase systems including peptides, proteins and materials. The initial appointment is for one year with the possibility to renew for a second year.
Recent and Related Publications:
D. Koner and M. Meuwly
Potential Energy Surfaces for Polyatomic Molecules from Energies and Gradients Using Reproducing Kernel Hilbert Space Interpolation J. Chem. Theo. Comput., accepted (2020); arXiv:2005.04667
Mike Devereux, Marco Pezzella, Shampa Raghunathan, Markus Meuwly Polarizable Multipolar Molecular Dynamics Using Distributed Point Charges
D Koner, MS Salehi, P Mondal, M Meuwly
Perspective: Non-conventional Force Fields for Applications in Spectroscopy and Chemical Reaction Dynamics J. Chem. Phys. (2020)
OT Unke, M Devereux, M Meuwly
Minimal distributed charges: Multipolar quality at the cost of point charge electrostatics J. Chem. Phys. 147 (16), 161712
Interested candidates should send a letter of intent outlining their past research interests and accomplishments and their future interests, and a C.V. (both as pdf and by e-mail) to m.meuwly»at»unibas.ch, and arrange for at least two letters of recommendation sent to the same mail address.
Prof. M. Meuwly Phone: ++41 61 267 38 21
Department of Chemistry Fax: ++41 61 267 38 55
Klingelbergstr. 80 Mail: m.meuwly»at»unibas.ch
CH-4056 Basel www.chemie.unibas.ch/~meuwly