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qualifications: Experience with implementation or applications of large machine learning models Experience with generative methods for protein design and/or docking simulations or generative methods
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benchmarking of large language models (LLMs). The research profile of the group is heavily machine-learning oriented and the group has access to excellent HPC infrastructure. For more information about LTG
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machine learning. Colourbox via Unsplash Colourbox What skills are important in this role? The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities
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candidate to develop Machine Learning models and frameworks for time series analysis, aimed at understanding how the human brain encodes information. This cross-disciplinary project is a high-level
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Integrated Circuits or Automation. Background in computational optics, inverse scattering algorithms, label-free quantitative tomography algorithms, optical simulations, image analysis or machine learning
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Processing and Image Analysis Group, Section for Machine Learning, Department of Informatics. You will be part of Visual Intelligence and the DSB group. For more information about the position see https
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports. You are suited for this position
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environment spanning physics, developmental biology, advanced imaging, and machine learning. Colourbox via Unsplash Colourbox What skills are important in this role? The Faculty of Mathematics and Natural
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statistics, high performance programming, machine learning and using data to constrain cosmological models. The first project opportunity will be how to use the distributions of large underdone regions, cosmic