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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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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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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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technologies like normalizing flows, graph neural networks, and transformers to represent distributions over trees, to improve MSC estimation. These technologies have shown significant improvements in
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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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data. For this, the applicant will generate in silico datasets from diverse computational models of development, such as models of tooth development and gene regulatory networks. They will leverage
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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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learning, deep learning and relevant software framework (R and Python) is highly desired. Very good oral and written communication skills in English are required. Emphasis will also be given on personal
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standard Python ML libraries (e.g., PyTorch) and software development tooling (git and docker) is preferable. Experience in the application of AI and Machine Learning in the analysis of scientific data