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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description
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are looking for candidates with a PhD in Computer Science, Visualization and Media Technology, Machine Learning or a closely related research field. A strong background in machine learning and visual data
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groups working towards a common goal. For this postdoc project, we seek a dynamic and motivated candidate with an interest in computational electromagnetism, inverse design, and/or machine learning in
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. Project overview The project involves applying advanced statistical analysis, machine learning techniques, and modeling approaches such as agent-based modeling to analyze diverse climate and socioeconomic
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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the fields of information visualization / visual analytics as well as machine learning in close collaboration with ISOVIS members, other research groups of the department, and domain experts within DISA
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, machine learning, molecular biology, or a related field is required. If not yet completed, please provide the expected completion date. A computational background and the ability to code independently in
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pollination services for crops and ecosystems. Current advancements in machine vision and machine learning, have drastically improved potential of monitoring capabilities. The Department of Energy and
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, 2018, 2018, 2025, Curr Opin Chem Biol 2015, ChemEurJ 2019, 2025, Nat Meth 2023). This project will combine CAR-T cell engineering with chemo-optogenetic systems to enable precision CAR-T therapy
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are looking for candidates with a PhD in Computer Science, Visualization and Media Technology, Machine Learning or a closely related research field. A strong background in machine learning and visual data