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reconstruction, data inference and fusion using classical and physics-informed machine learning approaches to advance time-resolved morphological and tissue property imaging of the heart The work will involve
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(Full Professor). Your activities Teaching The succesful candidate will be expected to teach 4 hours per week at the Bachelor's and Master's levels during the pre-tenure period (6 hours per week after
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on developing new generative modeling approaches, scalable training algorithms, and foundation model technologies. The role is suited for candidates with a strong machine learning background who are excited
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with motor impairments. Please visit the .NeuroRestore website www.neurorestore.swiss to learn more about our mission. Main duties and responsibilities Supporting .NeuroRestore researchers and clinicians
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PhD in one of these areas. Candidates should have an exceptional academic record, a strong background in machine learning, and a robust mathematical foundation. They are also expected to have strong
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environment for doctoral researchers interested in longitudinal and mixed-methods approaches to language learning and mobility.
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rough-surface and multiscale mechanics. In line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning
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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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departments) and combine it into measures that show what teaching and learning conditions actually look like across the university. The Rectorate and the departments use these analyses to gain a systemic view
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through strategies such as reinforcement learning (RL) and innovative frameworks like the "graph of thoughts". We are firmly in the "Age of Computation", where breakthroughs in AI are synonymous with