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thermochemical reactor process for heat storage, characterizing its behaviour and thermal performance. Post-doc Objectives: A prototype, representative in scale of the final system (several kW and several kWh
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. The work spans the full pipeline: establishing a rotating-bearing test bench; instrumenting it with different sensing modalities such as vibration, acoustic, and thermal sensing; and developing machine
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, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus
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microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM components are in use