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Field
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data Development of algorithms for infection and evaluation of infection hotspots in the plant population Coordination of the scientific interface to the project partners with regard to entomological
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Join our multidisciplinary research team to develop and apply machine learning and bioinformatic algorithms in biomedical research. This PhD project will focus on developing machine learning
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reinforcement learning and evolutionary algorithms to balance emissions reduction, safety, commuter convenience, economic factors and policy evaluation to generate optimised sustainable mobility plans. Decision
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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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strengthen your application: Experience with computational ship hydrodynamics Experience with STAR-CCM+ and Abaqus Knowledge of Control Algorithms (e.g. PID controller) Knowledge of Reduced Ordered Models What
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advanced algorithms that align, merge, and aggregate datasets while maintaining data fidelity, the project contributes to the CAMS goal of enabling precise, accurate, and actionable analytical insights
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older adults. The expected outcome is the creation of AI algorithms to detect early signs of neurodegenerative disorders in older adults living independently at home. The potential benefit is early
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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
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. Examples of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world
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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted