All life processes are based on the fact that biomolecules can exist in and switch between different structures that are associated to a specific biological function, and that they can bind other biomolecules or small molecules to assemble larger complexes that give rise to cellular function such as metabolism, neurotransmission, replication etc. Experiments are expensive and very limited in their ability to trace molecular structure and dynamics simultaneously. Atomistic molecular dynamics (MD) simulations are our best bet as they describe the molecular properties relevant to molecular function, and are becoming mature enough to be quantitatively reliable. However, the usefulness of MD is limited because the direct simulation of a single millisecond process in a protein would require decades or centuries on a modern supercomputer.

We have developed methods that allow to speed up the simulation process by orders of magnitude without significant accuracy loss. In this project we aim at exploring the association of proteins and their ligands with atomistic models using state-of-the-art tools in molecular dynamics simulation and Markov modeling.

For this project, we seek a candidate for a doctoral thesis. We are looking for a creative and independent mind with scientific aspirations, inert motivation and outstanding work ethics. The candidate will be embedded in a highly motivated and innovative team of computational physicists, engineers, chemists and mathematicians. We are closely collaborating with experimental partners in Berlin and outside, so open-mindedness and good communication skills are required.

The ideal applicant holds a degree in a quantitative science (Physics, Chemistry, Mathematics, Engineering), with excellent grades and will understand the statistical physics and numerical algorithms in depth. He or she has proven experience in developing software (e.g. python, C, Java) and is familiar with a Linux-like working environment.

Compensation will be according to the E13 salary scale (67%).

Applications to:
Frank Noe [log in to unmask]

Topics include:
- reaction diffusion dynamics
- Markov state modeling
- molecular dynamics simulations of protein-protein and protein-ligand binding
- new methods in computational and statistical physics

Please send applications (including transcript for students) to [log in to unmask]
More about our research: www.research.franknoe.de

Representative publications from our group:
1) PyEMMA Software: www.pyemma.org. See also M Scherer et al: PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models. J. Chem. Theory Comput., 11 . pp. 5525-5542.
2) JH Prinz, H Wu, M Sarich, B Keller, M Senne, M Held JD Chodera, C Schütte and F Noé: Markov models of molecular kinetics: Generation and Validation. J. Chem. Phys. 134, 174105 (2011)
3) F Noé, C Schütte, E Vanden-Eijnden, L Reich and T Weikl: Constructing the Full Ensemble of Folding Pathways from Short Off-Equilibrium Simulations. Proc. Natl. Acad. Sci. USA 106, 19011 (2009)
4) Plattner, N. and Noé, F. (2015) Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models. Nat. Commun., 6 . p. 7653.
5) A Mey, H Wu, F Noé: xTRAM – Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states. Phys. Rev. X. 4, 041018 (2014)
6) Schöneberg, J. and Noé, F. (2013) ReaDDy - a software for particle based reaction diffusion dynamics in crowded cellular environments. PLoS ONE, 8 . e74261. ISSN 1932-6203

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