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-based processing. This project will investigate event-driven learning approaches in the context of RL in an event-triggered fashion. Data efficiency will be improved by using meta-learning and pre
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, mathematics, computer science or similar; Solid mathematical and analytical skills, including signal processing, optimization, or information theory; Experience in programming, e.g., in C++, Python or Matlab
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their CV, their details may be used anonymously for the purposes of governmental annual review. Application process Applicants will be evaluated by an internal expert committee. Relevant applicants will be
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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decentralized (building-integrated) scenarios based on technical and economic criteria. Work in the EU Horizon Europe funded TREASURE project (www.treasure-project.eu) focusing on simulation models and the
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of thermodynamic modelling, process simulation is an advantage. For applicants to the PhD position, you must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent
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stacking, both high performance and (very) high density can be achieved by this concept. The mission will be to explore, understand and develop the fundamental physics of device operation. This will require
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Description: Broadleafification, i.e. the process of increasing the share of broadleaf tree species within conifer-dominated forests, is a promising strategy for biodiversity conservation in managed (hemi
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particularly West Antarctica, is crucial because this region is potentially subject to internal dynamic instability (marine ice sheet instability). If such a process were to be triggered, it could contribute
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enhance system reliability and safety, aligning with the UK’s NetZero targets. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess