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lake and river water levels. In parallel, socio-environmental signals on water-related extremes and pressures will be extracted using natural language processing and text-mining techniques, drawing
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a semi-industrial facility. In parallel, this PhD research will focus on investigating and optimising the process for removing arsenic from effluent, including through flocculation. Consequently
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requires an unprecedented computing and storage infrastructure. Processing these enormous amounts of data in a future data center, however, consumes significant resources. The fundamental challenge is: How
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(scientific Python, PyTorch) with solid experience in scientific computing, reproducible software development, and modern software engineering practices; familiarity with C++, Linux, and high
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test these improvements in the existing 1D model Work closely with Wageningen University, including several research stays in the Netherlands Make use of PING/MIAI computing resources and AI tools
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professor will also be considered. These positions are supported by the Data, AI, and Computing Initiative, and the selected candidates will be affiliated with this Initiative and/or its research
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and sufficient. Assigning meaning to prototypes is however a time-consuming task, as the various models tend to learn prototypes that do not carry sufficiently high-level meaning. A parallel line of
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position combines MLOps engineering, research software development and technical coordination across cloud, High-Performance Computing (HPC) and federated research environments. A key focus will be the
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computing circuit architecture Contract duration (months) 5 to 6 months Job description Context: As part of our ASIC design activities, we are developing an embedded computing platform based on a
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, research software development and technical coordination across cloud, High-Performance Computing (HPC) and federated research environments. A key focus will be the development of automated and