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Field
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for computing properties of nuclei. Prepare nuclear data for consumption by machine learning models. Investigate the use of machine learning models to compute properties of nuclei using high-performance computing
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that is helping to catalyze and support researchers in other countries leading national decarbonization modeling studies inspired by the NZA study. Currently, Princeton is collaborating on NZx studies
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) Country United Arab Emirates Application Deadline 13 Nov 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting-edge deep generative probabilistic models
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Deep learning models, and in particular large language
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. At present there is specific interest in advanced 3D perception techniques such as geometric foundation models, implicit neural rendering (NeRF, Gaussian Splatting) as well as semantic mapping. Our research
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models for microelectronics materials Curate, manage, and integrate heterogeneous datasets from experiments and simulations Collaborate closely with experimental teams to benchmark and refine computational
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partnership with the Qatar Foundation for Education, Science and Community Development. WCM-Q seeks candidates for a postdoctoral associate position in Mathematical Modeling of Communicable and Non-communicable
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global climate action through modelling and data-driven Antarctic research 3 year fixed-term full-time role based in Hobart with relocation assistance considered About the opportunity We are looking for a
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Generative Modelling to apply and develop cutting-edge deep generative probabilistic models including conditional diffusion and flow matching models for synthesising Magnetic Resonance Imaging (MRI) and