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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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the admission requirements for a PhD at ETH Zurich Experience in machine learning, optimization, or AI-driven decision-making Preferably with knowledge of Bayesian optimization or Gaussian processes
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recovery trajectories and injury patterns. Integrate personalized physiological measurements into a recovery prediction model, while adapting Bayesian Neural Networks for SCI data and analyzing the impact on
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the interrogation of chiral systems down to the nanometer scale. Project background Chirality describes whether an object is non-superimposable with its mirror image. Despite being a purely geometric property
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culture. Method development: Shape parameterisation; learning objective functions for mechanism design; build end-to-end pipelines using embeddings and generative models (e.g., GANs, VAEs, Diffusion
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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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. Utilize relevant technologies such as containers, object stores, and Kubernetes. Be versatile, display leadership qualities, and show enthusiasm for tackling problems and contributing to maintenance across
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documents A short statement of research interests and objectives. A CV including past research work and projects. 2-3 reference letters/contacts. One publication/thesis. Transcripts of all degrees in English
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-effector that incorporates a milling spindle a feed axis and integrated force-torque sensing capabilities. A key objective is to redesign the final axis of the system to embed strain gauges that can
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statement of research interests and objectives. A CV including past research work and projects. 2-3 reference letters/contacts. One publication/thesis. Transcripts of all degrees in English. Please note that