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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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Immunology and Computational Biology Reference number: 2025-0105 We are deploying advanced in vitro and in vivo model systems, genetic perturbations and single cell technologies with spatial readouts to study
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be self-motivated and have strong experimental background in molecular, biochemical, cellular, and histological analyses of cell culture and animal models. Please send a summary of previous research
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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background in immunology, tissue biology or cellular metabolism. Experience in working with animal models, cell isolations from tissues (lung, intestine), human tissue samples, multi-parameter flow cytometry
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investigating the molecular mechanisms of aging and the occurrence of age-related diseases. Our aim is to create the basis for new approaches in medicine as a way to improve the health of the elderly (www.leibniz
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reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling. Central to its
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling