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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 2 months ago
. Helmut Grubmüller) is inviting applications for a PhD Student or Postdoc (f/m/d) - Theory and Methods for Non-equilibrium Theory and Atomistic Simulations of Complex Biomolecules. Possible projects
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of Chemistry is equipped with state-of-the-art analytical, experimental, and data processing methods and is integrated into a strongly interacting and collaborating scientific environment with the Departments
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 1 month ago
track-record in first-author scientific publications for Postdoc applications Experience with data-driven machine learning methods for modelling (PINN, Sparse Symbolic Regression methods) High willingness
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Student or Postdoc (f/m/x) in the field of Theory and Methods for Non-equilibrium Theory and Atomistic Simulations of Complex Biomolecules Possible projects are variational free energy methods
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interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Control Theory Formal Methods Machine Learning Context The applicant will be directly advised by Prof
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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] Subject Areas: Multiphysics Simulation of Hemodynamics, Treatment, and Long-Term Perspective of Cerebral Aneurysms Using Lattice Boltzmann Methods. Appl Deadline: 2025/06/30 11:59PM (accepting
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materials with applications in heterogeneous catalysis, energy conversion and electrochemistry. By combining unique synthesis methods, state-of-the art tools for experimental characterization and advanced
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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essentially new methods to automatically verify cyber-physical systems. Because none of the existing methods and tools for the formal verification of cyber-physical systems are fully automatic, these methods