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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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of greenhouse gases including CO2 and CH4. The PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning for inteGrated multi-parAmetric
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– Adaptive & Agentic AI. The PhD project focuses on developing robust and reliable machine learning systems that can adapt at test time under real-world distribution shifts. Modern foundation models (e.g
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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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12 months by submitting a declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible. About the team Join the Responsible Machine Learning (ML
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experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image data analysis A willingness to engage in interdisciplinary scientific work
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infertility, pregnancy, lactation and developmental programming, urogynecology, artificial intelligence and machine learning are particularly encouraged to apply. The Department of Obstetrics and Gynecology
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below: PhD A – Importance of dark carbon fixation in the deep ocean. There is growing evidence that dark carbon fixation contributes to the sustenance of heterotrophic life at great depth. Using 14C and
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measure gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning