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Application deadline: 30/04/2026 Research theme: Nuclear Engineering How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD project is fully funded; home students are eligible to apply
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reproducible research practices. Your responsibilities Develop and implement computer vision and image processing algorithms for star tracking and satellite detec-tion using event cameras. Design and build a
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algorithms to analyze and recognize multimodal human behavior in real world settings (e.g., Affective Computing, AI for Healthcare: pain measurement, monitoring mental health disorders). The successful
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in applying experimental techniques to experiments at the LHC, in particular the use and operation of ultrafast time-of-flight detectors and exclusive production selection algorithms based solely
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. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ Data driven epidemiology and biology of infection cover research that will transform our understanding
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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research contributions will include designing algorithms for concept and structure extraction, building neural/graph hybrid models for pedagogical reasoning, implementing ontology-alignment methods for cross
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, the CSATLab , our SW Simulators , and our Facilities . For further information, you may refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role Develop innovative methods and data-driven AI tools
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of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test software tools, and
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working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems