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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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, 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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it fit to run on a supercomputer. You will apply your newly developed simulation tools within different research projects. This new position is embedded in the international research project LION 2 on
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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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with large language models (LLMs). The successful candidate will investigate both theoretical aspects – such as understanding the mechanisms and limitations of reasoning in modern LLMs – and practical
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responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling, or generative modeling. Collaborating with
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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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). 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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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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, at the phonetic, lexical and syntactic levels. The candidates will develop theoretical models of network of single neurons, using dynamical systems theory and simulations. The models will be fit to single-cell