187 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" "UCL" uni jobs at ETH Zurich
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bioelectronics or software simulations, or a strong willingness to acquire it, alongside solid knowledge of electrochemistry and materials chemistry Candidates should be eager to collaborate closely with molecular
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foundations of conversational AI as well as domain knowledge on how and why it can be deployed effectively. You should have knowledge and skills in both data analytics (e.g., machine learning, statistics
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experience working in collaboration with biological or clinical labs and with groups with a strong machine learning background. The starting date is by mutual agreement. We expect a pronounced interest in
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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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thinking with a structured, quality-focused approach to data and methods. Ideally, experience in one or more of the following: data engineering, building data-driven apps, computational linguistics, machine
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the environmental drivers that regulate these processes. We will use machine learning approaches (XGBoost, SHAP analyses) for the flux partitioning, complemented by existing tree dendrometer and sap flow measurements
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Participation in and shaping a dynamic research team with supportive colleagues, fostering collaboration and mutual learning Access to state-of-the-art research infrastructure Your job with impact: Become part of
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sustainable and climate-neutral university You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
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to a sustainable and climate-neutral university You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and
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the power of both classical and quantum computing resources? How can we exploit or take inspiration from quantum physics to develop cutting-edge machine learning? Your work will encompass a diverse array of