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with rare and critical conditions due to a lack of physical grounding. This PhD project aims to enhance traditional machine learning methods by integrating physics, empirical rules, heterogeneous
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the composition of planetary bodies. Job description The PhD project will include geochemical and mass spectrometric investigations to determine nucleosynthetic isotope variations in extraterrestrial samples with
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100%, Zurich, fixed-term Human–Computer Interaction in Architecture and Digital Fabrication This fully funded, full-time PhD position spans four years and is embedded within the interdisciplinary
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of machine learning, AI, and cancer genomics. Our lab develops novel machine learning methods to understand biological systems and cancer, with a strong focus on genomics and translational impact. We work in
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energy transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal
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connectivity Profile PhD in neuroscience, biology, bioengineering, or a related field Strong background in cell culture, including primary neurons or iPSC-derived cells Experience with electrophysiology (e.g
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space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where conventional
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methods. Contributing to the development, adaptation, and application of machine‑learning models tailored to RODI data (in collaboration with project partners). Designing and implementing an innovative
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Computer Vision and Computer Graphics techniques to digitize human avatars and garments in 3D. Within this project, your role is to advance our existing algorithms that reconstruct 3D garments from multi
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models at process scales, the project combines efficient mathematical concepts like automatic differentiation with backpropagation – the same concept that powers machine learning and artificial