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an individual research project, you will be responsible for setting up and evaluating data analysis, implementing models and networks, define optimal scientific protocols, and write scientific articles and
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informatics. This, to serve the biological goal of mapping out the breast cancer tumor microenvironment, understanding the regulatory signaling network, and identifying early stage progression markers and
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networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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Networks. The project encompasses several challenges in the gene regulatory network (GRN) field, from simulating realistic networks and data to accurate inference of GRNs from noisy gene expression data
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, wildlife management, forestry and crop protection. The Evolving Networks Lab is part of the Insect Ecology unit Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about
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: Master’s degree in biomedicine or biostatistics. Doctor of medicine degree with clinical practice experience. Certified training in R and Python software. Documented experience using machine learning and
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program (Data-Driven Life Science) with focus on precision medicine. Access to top-level infrastructure, a new therapy development initiative for brain diseases (CNSx3), and a strong network spanning
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different conditions using existing software (written in Fortran). Analysis of data using quantitative genetics tools (e.g., calculation and comparison of genetic and phenotypic covariance matrices
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the past ten years thanks to artificial intelligence, mainly in the form of deep convolutional neural networks. In parallel, functional analysis of tissue samples via novel microscopy techniques and spatial
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of cancer cells. The models are trained on high-throughput datasets, including metabolomics, proteomics, and transcriptomics, and constrained to align with the cell’s molecular networks. This allows us to