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Field
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You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
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environments.DutiesThe PhD student will carry out research in the area of cooperative autonomous systems. The successful candidate will explore topics such as: Multi-agent reinforcement learning Distributed control
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control in full technical cells – humidity, temperature, pressure drops, voltage drops Supervision of university students Participation in project meetings Coordination with internal and external partners
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into advanced turbofan configurations. This doctorate will research highly innovative technologies that carefully control the temperature of key engine components. The Oxford Thermofluids Institute
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Your Job: As part of a collaborative research project funded by the Federal Ministry of Research, Technology and Space (BMFTR), and conducted in cooperation with academic and industrial partners
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(subproject B4 - Multiobjective optimization of traffic signals for multimodal traffic): This project aims to develop a cooperative and adaptive traffic signal control approach that addresses the needs
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collaboration, transdisciplinary cooperation with practice and an overall dynamic development characterize its research profile in the fields of education, culture, political science, management and technology as
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generation by developing ML-based dual stabilization techniques. These techniques aim to predict and control the behavior of dual variables, reducing oscillations and improving the efficiency of the iterative
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary