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the challenging, open problems that matter most for how such models are used today. You may propose your own topic within the theme or start from one of the following directions: Uncertainty quantification
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earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc. Documented experience in machine learning, in particular deep generative
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We have the power of over 50,000 students and co-workers. Students who provide hope for the future. Co-workers who contribute to Linköping University meeting challenges of today. Our fundamental
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computational methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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conformational transitions induced by ligand binding, cofactors, metabolites, stress, or post-translational modifications. While recent deep-learning methods such as AlphaFold have transformed protein structure
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over 120 experts, you will have a key role in strategic and practical planning together with the SciLifeLab AI lead, and other national and international partners. Part of your time will also involve
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of data science tools such as Numpy, Pandas or Matplotlib is a merit, as well as experience of using modern deep learning frameworks such as PyTorch and Tensorflow. “Reproducible research” and “FAIR data
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program AlphaCell is a new SciLifeLab initiative aimed at building the first molecular-level computational model of the human cell. The program is led by Jan Ellenberg (Director of SciLifeLab) and Mathias
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predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will