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objective of the project is to develop knowledge, models, and algorithms for physics‑informed autonomous control of heavy machinery in uneven and deformable terrain. Specific project tasks include fundamental
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global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as
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(glycans) known as the glycocalyx, which is essential for multicellular life. Glycocalyces accomplish critical functions in inter-cellular communication, controlling tissue development, homeostasis and
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of the data used in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with
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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging