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university. More information about us, please visit: the Department of Biochemistry and Biophysics . Project description Project title: Biology-informed Robust AI Methods for Inferring Complex Gene Regulatory
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genealogical relationships and genetic divergence across species, but its complexity requires new methodologies for efficient analysis. This project aims to use Variational Inference (VI) methods, enhanced by AI
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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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Python or R are required. Basic understanding of statistical analysis in large dataset is expected. Fluency in written and spoken English. Merits: Experience in applying machine learning and/or network
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) strategies, primarily revolving around interpretable ML and generative AI, to study complex biological processes. This project combines timely analytical challenges with deep rooted applications in life
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Uppsala and in Sweden at large. For information about the SciLifeLab fellow program, see https://www.scilifelab.se/research/#fellows. SciLifeLab Fellows are also part of a broad national network of future
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become a life-saving option for advanced cancer patients. However, only a minority of patients develop a durable response. Many researchers are investing efforts to understand the complexity of anti-cancer
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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
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preprint servers) Experience in working with noisy data and managing multiple confounding factors Experience in applying machine learning and/or network analysis in complex datasets Experience of using
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provides excellent access to research infrastructure, including databases, computational facilities and instrumentation, as well as to clinical materials and networks and training activities. The four