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the bacterial import process, optimizing the system for high performance, and then applying it to problems around therapeutic peptides and proteins. The focus of the work is experimental and will include a broad
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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