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Field
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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party
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of the postdoctoral researcher will include: To work closely and proactively with Prof Anton van den Hengel to scope and develop research ideas. To develop algorithms, machine learning models, Python modules
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committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa. UM6P is an institution oriented
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development of detectors sensitive to ultra-high dose rates. Developments of computation methods for small-field dosimetry, radiation detection systems used in photon, proton, and electron beam radiation
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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ideas. To develop algorithms, machine learning models, Python modules, demonstrators and training pipelines for publication and translation into commercial products that can be widely and reliably adopted
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forecast modeling and scenario development for climate change and water allocation. The successful candidate will leverage existing water quality algorithms and products (e.g., temperature, turbidity
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-film solar cells. LightTrap covers the entire discovery chain, from ab initio calculations and photonic simulations to materials synthesis and nanostructuring, all guided by advanced AI algorithms
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on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems tailored to dynamic, human-centered environments. They may also work with diverse signal modalities, including vision
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of application development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). A record of productive and creative research as proven by