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the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based
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, machine learning, and statistical modeling on cutting-edge datasets in precision feeding, animal behavior and welfare, multi-omics and environmental impact. Join our interdisciplinary team to drive real
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Researcher or experienced Data Scientist to harness AI, machine learning, and statistical modeling on cutting-edge datasets in precision feeding, animal behavior and welfare, multi-omics and environmental
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by two ETH professors from two different departments. If you don’t have hosts yet, Design++ can help match you based on your proposal. Project background You are a great fit if you’re excited to: (a
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on studying shape parametrization, learning gait optimization functions for mechanism design and using different machine learning embeddings (such as GANS, VAEs, and Diffusion Models) for developing a new full
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and backtests to assess model performance and estimate the tool's real-world impact. You will have regular check-ins with the project team at Stanford but will conduct the day-to-day data work yourself
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postdoctoral researcher, your responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling
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). Many experimental projects are motivated by theoretical questions, use highly quantitative techniques, and are integrated with theoretical modeling and analysis of the data. Successful candidates should
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(CPU/GPU), numerical modeling/Monte Carlo simulations are an asset Visualisation skills are an asset Careful way of working, checking of results Candidates can have an M.Sc. degree in STEM, or a Ph.D
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collaboration with key disease biology experts in a strong internal and external network. You enable the generation of state-of-the art cellular models and cell-based assays through your scientific excellence