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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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control with Git, typesetting with LaTeX, use of Linux computers; Experience with convolution and transformer-based neural networks for image analysis; Experience with graph-based methods, and graph
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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and
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offers a unique interdisciplinary training environment bridging experimental mass spectrometry-based proteomics and AI-driven protein structure modeling. The student will have access to existing large
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on community ecology. Researchers have access to excellent glasshouse and climate-controlled facilities, fully state-of-the-art molecular labs and a high-performance computing cluster (UPPMAX). The Department