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materials that underpin device operation (see https://doi.org/10.1016/j.joule.2023.03.002 for a recent example of this approach from the group). This project will be carried out in collaboration with the
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machine-learning methods to identify and quantify species interactions from acoustic recordings. Construct ecological interaction networks from the inferred acoustic data. Contribute actively to scientific
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PhD student in generative modeling for data-efficient machine learning Norrkoping Reference number LiU-2026-02831 We are now looking for a PhD student in machine learning with a focus on generative
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, computational science, data science or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in ecology, biology, applied mathematics, computational science
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learning, programming, and analyzing visual data. Furthermore, you have the ability to work long-term and move projects forward even when challenges arise. You enjoy working towards results and
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using advanced data analysis techniques, and contribute to the development of new computational tools and methodologies. The research combines fundamental fluid mechanics with modern AI methods and
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construction of high-dimensional optimal portfolios. Motivated by the widespread application of sample generalized inverses in practice when the dimension of the data-generating process exceeds the sample size
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data collection. The PhD student will also study how key concepts such as trust, utility, risk, and responsibility are shaped in relation to swarm technologies (in air, water, and on land), and