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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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they help orchestrate the production and learning of dexterous movements. By gaining a better understanding of these fundamental mechanisms, our ultimate goal is to develop new therapeutic treatments
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learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k – 57k
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learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k – 57k
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experience and profile: • PhD in machine learning theory, with thesis submitted before the start of the Postdoctoral position. • Publications on machine learning theory in proceedings of international machine
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in an area of safe machine learning and/or applications in healthcare Management of a team of PhD students, postdocs, and software developers Coordination of the implementation of research prototypes
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we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will
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of substitution models using large dataset, successful applicants must then have a PhD and demonstrated experience in discrete choice models, machine learning techniques, big data, and optimization
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project “ALPS - AI-based Learning for Physical Simulation”. Expected start date and duration of employment This is a 2–year position from 1 October 2025 or as soon possible. Job description You will be
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. This may involve techniques such as spatio-temporal regularization, discrete tomography, low-dimensional latent representations and machine learning. The ultimate aim is to reduce the carbon footprint