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, including high-throughput screening, high-content imaging, omics technologies, and computational approaches, to elucidate mechanisms of toxicity. Ultimately, our work contributes to a deeper understanding of
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on developing methods for the verification and validation of systems that embed machine learning or generative models, addressing challenges such as non-determinism, data drift, and explainability. The project
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subject is divided into a range of sub-disciplines and specializations. The PhD program at the Department of Biology includes many of these specializations, from molecular biology to applied ecology, from
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millions of species. However, computational challenges exist in using these massive genomics datasets for species delimitation. The multispecies coalescent (MSC) model offers a framework for understanding
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and disruptive business models, informatics’ logic for systems- and business development, digital platforms and digital eco-systems, and digital sustainability. The duties also involve course
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partners in research and education. The department works together with the neighboring departments for a creative environment with challenging educational program and cutting-edge research. Research in
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learning models—alters the way software systems evolve (SE4AI). A strong focus will be on the post-deployment lifecycle of ML components, including drift detection, model decay, retraining triggers, and
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offers a bachelor’s program in building technology, a bachelor’s program in building design, and a two-year master's program in sustainable construction technology. We are also developing an innovative 5
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initiatives: ISWIM – Innovative Stormwater Impact Mitigation, supported by Formas, and REWAISE – Resilient Water Innovation for a Smart Economy, supported by the EU Horizon 2020 program. Both projects
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Assistant Professor in artificial intelligence (AI) with a focus on precision medicine (PA2025/1776)
multifactorial backgrounds. Today’s disease risk prediction models and preventive interventions designed to reduce such risk are not precise enough. Lund University expects approaches with advanced AI methods