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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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/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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nano-scale objects Designing and building a coherent low-energy electron microscope Sample preparation and recording holograms Numerical reconstruction of the sample structure Presenting the results
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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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to the nanometer scale. Project background Chirality describes whether an object is non-superimposable with its mirror image. Despite being a purely geometric property, chirality plays a crucial role in all living
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the position may begin as late as June 2026, upon request. The duration of the position is 4 years. Your tasks include: Conduct research in line with the research project objectives. Write high-quality research
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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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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