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, precision livestock farming, and multi-omics approaches toward sustainable dairy farming, leveraging advanced statistical modeling, machine learning, and AI to uncover biological insights and optimize
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100%, Zurich, fixed-term We are seeking a skilled Machine Learning Engineer to join our dynamic team. The ideal candidate will be involved in the development, optimization, and maintenance of our
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the Multiphase Fluid Dyamics group (Prof. Supponen) has a focus on modelling and high-speed imaging to optimize bubble generation and control, and the resulting particle dynamics and interaction with cells using
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. The optimal exploitation of digital tools in system development processes facilitates an effective and efficient process. To effectively capitalise on the existing knowledge base, digital tools, e.g., large
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hands-on learning experiences or with computational elements relevant to Artificial Intelligence or Optimization, in addition to running your research group. Since this is a leadership position, your
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would build upon the laboratory's past and ongoing work within the context of AI-guided Design, Inverse Design, and Optimization across different application domains. In particular, we are developing
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. Additional familiarity with building your own embedded sensing sensing setup is a strong plus (e.g., for EMG/ECG/EOG or other differential or regular sensors). An optimal background for the project would be
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-armed Bandits, Bayesian Optimization. Automated Model Design and Tuning: Neural Architecture Search, Hyperparameter Optimization. Computer Networking: Resource-Constrained Networking (e.g., Internet
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into functional cellular therapies. Job description Job responsibilities include engineering, transducing, and characterizing primary T cells with synthetic genetic circuits; optimizing and executing protocols