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automation to enable closed-loop, self-optimizing experiments Initial experiments will investigate phase equilibria and transport processes in liquid mixtures The work will be conducted in tight integration
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and institutions that interact with them. You should bring: A master’s degree in computer science, machine learning, HCI, or a related field. Strong foundations in machine learning and familiarity with
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-prevention antibody/nanobody program from discovery toward the clinic (start immediately or by arrangement). Your path: from fellowship to founding Over the 12 months you will drive the antibody program toward
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. Depending on experience and interests, the project may include: Development of next-generation focused ultrasound systems fMRI-guided treatment planning and targeting Optimization of targeted drug delivery
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- such as high-frequency on-board vehicle sensors and computer-vision imagery - are well-suited to capture these complexities via near real-time, high-resolution insights. However, collecting, processing, and
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or guideline-derived signals Reinforcement learning for oncology-specific reasoning behavior Comparison and development of RL training approaches Calibration, abstention, and safety-aware optimization Clinical
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work, you will publish your results in peer-reviewed journals and present them at international conferences Profile You meet the requirements for a doctoral program at ETH Zurich and have an excellent
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100%, Zurich, fixed-term The Molecular Engineering Thermodynamics (MET) group at ETH Zurich is looking for a doctoral student to develop and improve computational tools for the molecular scale
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to address placement of buffers design and implement a simulation-optimization view to evaluate tentative solutions, considering estimated delay dynamics and possible degrees of rescheduling, to improve
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, and discovering sustainable materials for biohybrid systems (including non-mammalian sources). Recent results from the lab include muscle–tendon bioprinting of mechanically optimized musculoskeletal