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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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explore new mathematical and computational approaches to represent such systems, drawing on ideas from stochastic processes, four-dimensional (space-time) modelling, complex systems, agent-based modelling
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Fusarium graminearum, the causal agent of Fusarium Head Blight (FHB), and the English grain aphid (Sitobion avenae), one of the most economically important pests of cereal crops. FHB causes substantial
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lessons from experiments and experience to shape AI-native software engineering pilot and transition improvements with organizations and measure their success create novel agents and agentic workflows
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lessons from experiments and experience to shape AI-native software engineering pilot and transition improvements with organizations and measure their success create novel agents and agentic workflows
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- specifically myeloperoxidase (MPO) and eosinophil peroxidase (EPO) - to generate powerful oxidizing agents primarily designed for the elimination of invasive pathogens. However, these highly reactive oxidants
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supervise university students. The department has an international staff of approximately 40 researchers, PhD students and postdoctoral fellows. We have a great focus on multi-disciplinary collaboration
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agentic software can change the way scientists interact with complex imaging pipelines – for example, by allowing users to formulate analysis tasks at a high level while software agents orchestrate
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https://wise-materials.org/research-school/ an ambitious nationwide program of seminars, courses, research visits, and other activities to promote a strong multi-disciplinary and international network
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Computational pathology and digital pathology using whole slide imaging (WSI) Spatial biology, spatial transcriptomics, and multi-omics data integration Large language models (LLMs) and foundation models