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the foundations for this. The ideal candidate should hold a PhD in a relevant specialist subject or is about to submit, or should have equivalent experience. A background in machine learning applied in
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preparation and testing or powder mixtures, and then to devise predictive models (possible using machine learning approaches) for the estimation of mixture properties from pure component propeties. The PhD
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& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The aim is to develop machine-learning models that describe how
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to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning