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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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) to conceptualize and quantify the controls of anvil extent and cloud feedback of tropical deep convection across scales. As a successful candidate, you will combine your expertise in deep convective processes with
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that might integrate spatial analytical methods Excellent command of the English language (written and oral) Strong organizational skills and ability to work independently Love for working in
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the advection, cooling, and drying of air masses from lower latitudes. To better constrain future Arctic climate change, we want to understand how boundary-layer and cloud processes drive and control these air
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to statistical analyze longitudinal cognitive data Experience with the preparation of scientific publications Result-oriented and independent approach to work High communication skills and good command of English
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crosslink highly diverse bioeconomic production systems and to control them in a knowledge-based, adaptive, and largely automated manner. We conduct research in dialogue with society - knowledge-motivated and
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, including next generation sequencing data processing is an added advantage excellent command of written and spoken English pro-active learning and desire for career development excellent communication and
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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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of dissolved organic substances, microbiological rate measurements, incubation experiments, controlled laboratory cultures, and field studies in various marine regions. The Research Unit Biological Oceanography
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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