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enabling process monitoring, learning, and policy training while ensuring safety and efficiency. For this, we develop novel learning-based control methods with practical applicability and theoretical
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PhD Stipend in Digital Twin Modelling and Multi-Timescale Optimization of Electrolysis Systems Apply Vacant position PhD Stipend in Digital Twin Modelling and Multi-Timescale Optimization of
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Eindhoven University of Technology (TU/e) | Eindhoven, Provincie Noord-Brabant | Netherlands | 2 months ago
uncertainties in the design process, yielding solutions that are less sensitive to perturbations and therefore more reliable in practice. Mathematically, this leads to optimization problems with
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instruments such as miniaturization of NV sensors. Experience in systems engineering and process optimization is preferred. Key Responsibilities: Conduct research activities related to product design and
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-based control layer. The project will connect agentic orchestration, automated experimentation, real-time analytics and data-driven optimization. The researcher will develop workflows in which
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response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems. The position will involve
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analysis, life-cycle assessment, mathematical optimization, process-oriented modeling of industrial energy systems, and the analysis of regulatory frameworks. In particular, we focus on chemical
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will include process monitoring, estimation, analysis, optimization, and control policy development. The project requires hands-on expertise in ML, time-series forecasting, modelling, and control of
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, and advancing automated micro‑scale parallel experimentation workflows useful for the optimization of new reactions and for the high-throughput synthesis of libraries for biological screening. The
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Infrastructure? No Offer Description Job description Game theory is rapidly gaining traction in several engineering applications as the natural framework for multi-agent decision making. Yet, unlike optimization