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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Develop new machine learning
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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Helmholtz-Zentrum für Infektionsforschung GmbH | Braunschweig, Niedersachsen | Germany | 26 days ago
Germany As a member of the Helmholtz Association, the Helmholtz Centre for Infection Research, based inBraunschweig, conducts top-level research in the field of infectious diseases. Our scientists develop
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for therapeutic intervention. This PhD project will leverage large-scale single-cell RNA-seq and spatial transcriptomics datasets from infection biology to develop models, including transformer-/graph-based models
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microscopic (cell) scale. The position is available from May 1, 2026, but a later starting date could be considered. The position is funded for four years. Project background The available position is funded by
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sciences or similar Strong interest in single-cell nucleus RNA-seq techniques / Perturb-Seq / Crop-Seq Willingness to learn computational biology/bioinformatics High motivation for scientific work and
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of LNPs under various storage conditions, the aim is to develop formulations with both high performance and improved stability. The work includes the design and systematic screening of LNP formulations
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The Leibniz Institute for Neurobiology (LIN) is an internationally recognized neuroscientific research institute and dedicated to the research on learning and memory. Our research comprises all
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. Other data sources could be compiled to create climate analogues. 2) Develop a predictive model forecasting the future impacts of climate change on cardiorespiratory fitness in children and adolescents 3
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the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration