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to answer different kinds of questions. This PhD position focuses on understanding, interpreting, parsing and formalising geo-analytical questions. You will explore hybrid (symbolic and sub-symbolic) AI
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research in some part of analytic number theory, automorphic forms and/or representation theory, building on recent advances in these subjects. Qualifications Applicants should hold a PhD degree in
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chemistry, sustainable chemistry, and organic chemistry. PhD programmes are conducted in five areas: analytical chemistry, physical chemistry, materials chemistry, inorganic chemistry and organic chemistry
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the PhD Students: - Decide the experimental design for the validation of the developed models to authenticate pistachios, and collaborate in recruiting new samples - Perform the analytical
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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a highly motivated PhD candidate to advance predictive, data-driven production and
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Unlock how geographic data can answer complex societal questions: in this PhD you develop semantic and AI-driven models that transform diverse geodata into actionable answer maps. Join our ERC-funded
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Space Agency (RECCAP2, TRAC3E), and the ENSURE UEXE-CUHK joint centre (https://www.exeter.ac.uk/global/cuhkpartnership/). This fully funded PhD project is suited to candidates with a related research
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to ensure analytic-ready datasets, secure data pipelines, and proper documentation. • Guide data cleaning, variable construction, coding, and data integrity checks. • Ensure fidelity to data governance
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, plate structures, semantic data models, finite element analysis, and plugin development. The PhD position (full-time) will span 4 years of research and is funded according to standard SNSF regulations