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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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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning, and
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Pneumatic Tires, Structure-Process-Properties Relationships. How will you contribute? Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling? You
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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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fields: AI (machine learning, big database, etc) Semiconductors
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, you must have a PhD in a relevant field. As a suitable candidate, you have expertise in deep convective cloud processes and experience with scientific data analysis. Prior experience in applying machine
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We