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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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artificial intelligence methods. PhD position in atmospheric corrosion studies via novel experiments and machine learning Reference code: 50134137_2 – 2025/MO 1 Commencement date: as soon as possible Work
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/or spatial multiomics, advanced imaging, iPS cells, machine learning, and computational biology. The ideal candidate will have a passion for addressing fundamental questions in biology and an eagerness
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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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the contribution of genetic and non-genetic driving forces for the cells’ evolution and glioma development. Using multi-omics data integration and machine learning, we will investigate cellular
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researcher with a proven track record in areas relevant to auto-tuning, focusing on ML-driven compiler optimization, transfer learning, and programming for heterogeneous systems across CPUs, GPUs, and
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materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials synthesis and
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dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive salary in one of Germany’s most attractive research environments. TUD is one of eleven
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opportunities to learn, develop, and apply machine learning and deep learning methods on genomics data. Requirements: excellent university and PhD degree with experience in molecular biology, computational
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage