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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
using first-principles simulations software (density functional theory and related codes) # Automated Workflows:Utilize automated workflows on high-performance computing systems for efficient data
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, Statistical Physics, Genome Annotation, and/or related fields Practical experience with High Performance Computing Systems as well as parallel/distributed programming Very good command of written and spoken
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(university diploma or master's degree) in the field of geosciences, engineering or physics. Ideally, you have knowledge of numerical methods and experience with common programming languages (e.g. Matlab
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or related fields Expertise in advanced neurofunctional assessments and methods (e.g., computer vision, bioacoustics, machine learning/AI, neuroimaging, eye-tracking, multimodal data fusion) Excellent
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Physical Oceanography and Instrumentation, Marine Chemistry, Biological Oceanography, Marine Geosciences, and Marine Observations works interdisciplinary within a joint research program. What will be your
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bases and instruments in terms of their efficiency and incidence Applied game-theoretic modelling based on 1) and on numerical estimates of the benefits that potential donor countries derive from
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the Phytophotonics department, you will work as part of an interdisciplinary team at the interface between plant sciences and optical technologies. The focus is on various spectroscopic and imaging methods
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processes, and the application of AI methods in engineering. Description: Nowadays, computer-aided manufacturing (CAM) methods are used to a large extent for the production of complex machine components, in
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ecosystem models. Experience using high-performance computing systems. Proficiency in running numerical ocean models. Familiarity with operating systems such as Linux/Unix and proficiency in shell scripting
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and to identify new therapeutic targets and prognostic biomarkers. Novel data integration methods allow to identify signatures of retinal cell types in liquid biopsies without the need for a direct