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energy system models that incorporate a stronger Social Sciences and Humanities (SSH) perspective. By embedding societal dynamics, such models aim to capture a wider range of future uncertainties and
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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of systems. We develop methods to advance sustainable energy and chemical process systems from the molecular to the scale of the systems. Our work combines process analysis and system modeling to design
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-scale electrode modelling and numerical upscaling techniques to the continuum scale will be applied. Finally, the aim is to develop a cell scale model using a macro-homogeneous description of the porous
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understanding of district heating and cooling, renewable energy integration, multi-energy systems, and energy conversion and storage technologies. You have strong skills in programming, modelling, and data
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lasting for 36 months. SPACER aims to develop new architectures for porous electrodes to improve the power density and energy efficiency of redox flow batteries (RFB), enabling affordable and durable long
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Position: Bioprinting next generation functional tissues The field of tissue engineering and bioprinting is continually advancing to develop functional tissue models that more accurately mimic native tissue
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of labeled phospho-tyrosine. Your tasks will encompass utilization of state-of-the-art bionformatic tools for protein design, perform high-throughput cell uptake tests, perform selection assays and express
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alcohols The Laboratory of Energy Science and Engineering is looking for a candidate to fill a PhD position to develop catalysts for the conversion of CO2 into higher alcohols. At LESE, we aim to advance