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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
to collaborative software development and version control systems (Git) # Experience with electronic structure and molecular dynamics simulation codes (VASP, QuantumEspresso, CP2K, LAMMPS) # Motivation to work
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states Investigation of non-equilibrium dynamics relating to quantum simulation or pump-probe experiments Machine learning for feedback control of monitored quantum systems Participation in international
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the German Baltic Sea coast Analysis of changes in locations and coverage during the last decades as well as the controlling factors Hydrodynamic model-based assessments of the drivers of changes in seagrass
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Solid State Physics. Your tasks: The successful candidate (m/f/d) will conduct theoretical studies of phononic/photonic control of out-of-equilibirium magnetic dynamics in quantum materials, exploiting
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., deep learning and statistical modeling). You have knowledge of molecular genetics and genomics. You have a very good command of English (both spoken and written). You have the proven ability to conduct
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groups. An excellent communication skill for scientific discussion is indispensable, and proficiency in English is mandatory. Command of German is an advantage, but not a requirement. Full applications
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research focuses on the study of quantum magnetism at the atomic scale. We build artificial quantum architectures from single atoms and molecules on surfaces and on probe tips, control their spin states, and
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 2 months ago
subject. Broad background in solids state physics and ultrafast optics. Excellent communication skills, and in particular good command of the English language. Hands-on experience with ultrafast and
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
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reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling. Central to its