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for large-scale pan-cancer multiomics data. We build on our previous work (e.g., Sanjaya et al. Genome Medicine 2023 ; Pohjonen et al. arXiv 2024 ), developing the new models on the LUMI supercomputer and
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chain network analysis and geospatial modeling. The successful candidate will have strong data science skills, including experience working with large, complex data from varied sources, and machine
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data (transcriptomics, proteomics and metabolomics), from a large collection of well-characterized samples to identify novel biomarker and causal and druggable targets. We are seeking a motivated
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experience with AI model development, including fine-tuning, applying and evaluating large language models for tasks such as information extraction, summarization, and other biomedical or clinical applications
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healthcare applications. We use large real-world complex datasets, including data extracted from electronic health records and medical images, for applications pertaining to patient diagnostics and prognostics
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the following documents in English: A curriculum vitae including a list of publications. Description of research experience (1-2 pages) Names and contact information of two referees, who are willing to provide
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Measurements and Data Processing as per October 1, 2025, or as soon as possible thereafter. The position is available for a period of 1 year, with the possibility of extension. In electronic engineering, Aalborg
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qualification in Genetics, Bioinformatics, Computer science, Data science, Statistical Genomics or a related discipline involving the interrogation of ‘omics’ datasets. Hands-on experience with large-scale human
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robust models – and for clinicians, whose goal is to determine when to trust the models. We therefore seek candidates who have strong technical background in working with large-scale deep learning models
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 1 month ago
of (a) (young) stellar clusters, (b) molecular gas and dust, or (c) star-forming sites (e.g. HII regions) in gas-rich galaxy centers. The work will encompass all aspects from data reduction to their in