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`liquid-liquid phase separation' (LLPS). We will use programmable, multi-component model systems of biomolecular phase separation to investigate the transport of biomolecular information, stress, and light
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on skills and interest): Build “incentive-preserving prediction models” for variables with positive global externalities, based on country characteristics (GDP, population density…) Develop procedures
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on the influence of Alzheimer’s disease and aging on changes in cognitive functions in humans. The project combines cutting-edge technologies from genetics, proteomics and statistical modeling to understand
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] Subject Areas: Applied Mathematics, numerical methods, simulation and modelling Appl Deadline: 2025/05/31 11:59PM (accepting applications posted 2025/02/13) Position Description: Position Description
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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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-dimensional cell cultures are important to enable realistic cell environments for disease modeling and to analyze cell interactions. This position will address the question of how one can develop minimally
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Excellent skills in statistical modelling, preferably using R Proven track record of publishing in international peer-reviewed journals as first author Willingness to conduct fieldwork and participate in
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Your Job: We are looking for a researcher to develop and apply machine learning models for genomic data in our lab. We focus on sequence analysis, genomics, semantics, and cross-domain data
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plan that focuses on the development and combination of 3D-generative models and potency predictions for drug design. A successful research proposal to our question will focus on these topics: How
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(including the doctoral dissertation) Strong methodological training in quantitative survey and experimental research (Additional asset: experience with using large language models in surveys) Proficiency in