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We are seeking a research assistant with a background in computing to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device
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of physics into machine learning and deep learning architectures to create accurate, physically consistent, efficient and interpretable/generalizable models. This PhD project will contribute to the development
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. The developed technologies will be validated in half-cells and full working batteries at industrial partners at TRL 6. Our objectives: Multiscale modelling to better understand RFB behavior and identify optimal
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. Foreseen challenges include data harmonization, key relationship extraction, scalability, metadata structuring, predictive modeling, and data quality assurance. The research will integrate structured and
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for pavement lifetime prediction, primarily in Dutch conditions. Foreseen challenges include data harmonization, key relationship extraction, scalability, metadata structuring, predictive modeling, and data
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shape responses to therapy. Therefore, this project aims to: (i) evaluate the efficacy of radio- and ICB therapy in preclinical mouse models of lung- and melanoma-BrM bearing different genetic makeups
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, trillions of cells of multiple different cell types communicate with each other by releasing a thousand types of molecules. However, the functioning of biochemical signaling does not seem to comply with
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Description Two PhD positions in atmospheric selenium research: field & lab analyses and process-based modeling We are seeking two highly motivated and curious PhD students to join the Inorganic Environmental
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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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detection using floor vibrations recorded by high-sensitivity accelerometers. Unlike wearable devices, which rely on user compliance, or cameras, which raise privacy concerns, floor vibration-based sensing