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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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interpretable models that connect molecular patterns across multiple scales of human biology. The project lies at the interface of artificial intelligence, computational biology, systems medicine, and precision
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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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part of the SciLifeLab and the research school of the Wallenberg National Program for Data-Driven Life Science (DDLS), within the research area Cell and Molecular Biology. To achieve this, the doctoral
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sciences Collaboration with experts in lab science, medicine, and machine learning Modern GPU compute infrastructure A chance to contribute to cutting-edge research with real-world impact Who you are Strong
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Collaboration Conduct research together with academic, industrial, and healthcare partners in a highly collaborative environment. Who we are looking for A highly motivated PhD candidate excited by computational
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effects. Importantly, computational predictions will be directly linked to experimental evaluation of GalNAc-conjugated RNA therapeutics. This position offers a unique opportunity to work in a highly
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experimental and computational methods to identify molecular mechanisms leading to dysfunctional cellular states in human disease (www.camunaslab.org ). The candidate will contribute to the improve Slice-seq, a
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, animals, fungi, bacteria, etc.). The Program for Plant Ecology and Evolution, where this position is located, is home to eight research group focusing on genomics, adaptation and speciation as well