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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models
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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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: https://www.slu.se/institutioner/vaxtbiologi-skogsgenetik/ Read more about our benefits and what it is like to work at SLU: https://www.slu.se/om-slu/jobba-pa-slu/ PhD Student: DDLS integrative
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methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen nomenclature, standardize laboratory test methods and result vocabularies, and translate clinical
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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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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at KTH in the Biotechnology doctoral program (according to KTH’s regulations). Signing a researcher agreement is a necessary condition to accept this PhD position. More information about Formulation of
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research staff of 180, of which 65 are PhD students. More information about us, please visit: the Department of Molecular Biosciences, the Wenner-Gren Institute . Project description Metabolic control