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will be embedded in the Computational Mechanics (Legato ) group and enrolled in the Doctoral Programme in Complex Systems Science. Depending on background and interests, the PhD may include: Development
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projects focused on real-world data analytics, health outcomes modeling, and biomedical informatics. Develop and apply advanced statistical and machine-learning methodologies to address complex health
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students contributing to the project Provide assistance in organizational matters related to the project PCS-Graphs Since autonomous robots need to operate in complex and ever-changing environments for long
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modeling techniques to complex biological and ecological systems, with applications in forest productivity, climate resilience, and sustainable resource management. Why Work at Auburn? Life-Changing Impact
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Analysis Synthesize and disseminate research findings by drafting accessible summaries, newsletters, and briefing memos; translate complex neuroscience and legal concepts for diverse audiences, including
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central component of this work is the development and application of machine-learning and AI techniques to identify weak, rare, or previously unknown nuclear transitions in complex spectral data. Advanced
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expertise in analysing complex quantitative and computational data and/or rich qualitative data. • Proficiency or skills to learn a statistical software (e.g., R, Stata, Python). • Interest in contributing
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of complex epidemiological and genetic data, in computational and population health sciences and in disease risk-modelling and risk-prediction. Eligibility criteria The project will suit students with strong
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and forest modellers within the FORFUS consortium, and will be embedded in the Doctoral Programme in Complex Systems Science at the University of Luxembourg. The modelling approaches developed in
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tools, work to identify and address emerging and complex threats to AI systems and effectively participate in the broader security community. Study and influence the AI security and vulnerability