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Field
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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algorithm development, numerical methods, or high-performance computing. Proficiency required in Python/C++ and scientific libraries (e.g., NumPy, SciPy). MATLAB is also desirable. Bonus: Familiarity with X
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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considered for this project. Application form Closing date 14 March 2022 Decision date May 2022 Additional information ¿Multi-criteria Automatic Algorithm Configuration under Streaming Problem Instances
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integral methods, stochastic modelling, optimisation algorithms, or numerical simulation. Candidates with interest in electromagnetic theory, quantum mathematics, wave chaos and complexity, as
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management of microgrids, battery scheduling, and handling uncertain renewable generation without relying on forecasted data [1]-[5]. Studies reveal that parametric tuning of RL algorithms such as state and
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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conservative transmission algorithms that ensure consistent exchange of information between subdomains. You will also develop and optimise parallel algorithms for large scale execution on HPC systems
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Full-time: 35 hours per week Fixed-term: 30th June 2026 Applications are invited for research associate positions in Algorithms and Complexity, funded by the European Research Council (ERC