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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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graph learning models, primarily geared towards assisting combinatorial solvers for practical graph algorithm benchmarks. Please find out more here: Dr. G. Rattan Information and application Are you
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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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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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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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graph learning models, primarily geared towards assisting combinatorial solvers for practical graph algorithm benchmarks. Please find out more here: Dr. G. Rattan Your profile You have, or will shortly
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to continuation as a researcher at Ericsson Research. Practical work tasks include: Developing algorithms and models for dynamic spectrum sharing using RDT data Implementing and evaluating signal processing and
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. You will be working at the intersection of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping
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will be developed. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also