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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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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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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and
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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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06.12.2021, Wissenschaftliches Personal The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation
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Postdoctoral Associate to investigate the neural mechanisms underlying continual learning in humans. The successful candidate will develop computational models examining the tradeoff between task
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. Machine learning/atmospheric science/satellite data processing experience preferred but not required Creative problem-solving skills and ability to work independently *Candidates with a PhD in other
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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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science. Your competencies PhD in computer science, statistics, data science, or a closely related field. Expertise in probabilistic machine learning, optionally generative and Bayesian models. A strong