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are you going to do? The project addresses a central question in mechanobiology: how do cells sense, encode and respond to mechanical cues? You will develop a quantitative and predictive framework
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of these models therefore remains a key scientific challenge. In this PhD project, you will develop and apply data-fusion methods that combine physics-based wind farm models with wind tunnel and field data. A
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. Ability to develop, understand, and critically evaluate machine learning research software, preferably using Python and PyTorch. An interest in foundation models, self supervised learning, multimodal
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reliable hydrogen-resistant circular steels. In this role, you will develop fundamental insights into the mechanisms governing hydrogen-induced degradation and failure of circular steels. As a PhD researcher
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tools, analyze their relevance for the hospitality industry and educational practice, and assess their pedagogical impact. Through applied research and evidence-informed experimentation, you identify
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, Robotics, Electrical Engineering, or a closely related field. A strong academic record and solid background in machine learning and deep learning. Ability to develop, understand, and critically evaluate
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that looks plausible but violates physics and is therefore of limited use for grid planning. As a PhD candidate you will develop physics-informed, domain-constrained generative models for energy-system data
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researchers and universities. The programme includes joint training, research seminars, methods workshops and opportunities to collaborate with partner institutions across Europe. A six-month secondment is
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The POLARIS training network POLARIS is a European Marie Skłodowska-Curie Doctoral Network focused on accelerating therapy development for leukodystrophies: rare genetic disorders affecting
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Employment 0.6 FTE Gross monthly salary € 3,202 - € 4,159 Required background Research University Degree Organizational unit Faculty of Social Sciences Application deadline 23 October 2026 Apply now