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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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QUALIFICATIONS PhD in Ecology, Geography, Environmental Science, or a closely related discipline. Demonstrated specialization in remote sensing and spatio-temporal environmental data analysis. Strong programming
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the university. Further information on the Department, its people, taught programmes, and research activities can be found at our main website, http://www.reading.ac.uk/economics/ , or our research-focused website
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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NordForsk 2026-2029 in a collaborative team of Nordic and Baltic NLP partners; see https://www.nordforsk.org/projects/culture-sensitive-assessment-and-adjustment-large-language-models-adaptation-nordic-baltic
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), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate
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the day) to accommodate your needs. Working remotely is possible for up to 1 day/week. About the Role The post is funded for 3 years and is based in the Big Data Institute, Old Road Campus, Oxford
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Postdoc in pharmacoepidemiology: Long-term safety and benefits of ADHD medication in children and...
. Core research tasks include planning, conducting and publishing epidemiological studies using large-scale observational data, primarily register-based, with a focus on effects of ADHD medication in both
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, particle physics, and mathematical physics. The Astroparticle and Cosmology group (https://astrocosmolapth.com ) conducts research on cosmic large-scale structure, cosmic microwave and infrared backgrounds
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position within a Research Infrastructure? No Offer Description AgrifoodTECH COFUND Research Code: C1-BIG DATA We are seeking innovative research proposals that aim to contribute to advances in food safety