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natural and highly polluted conditions, and how these processes can be represented in models to theoretically assess the potential of cirrus cloud thinning as a climate intervention strategy. The selected
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, metalloproteins' biochemistry, expression and purification of natural or artificial metalloproteins, from prior research work during PhD or postdoctoral training will be important criteria for selection. Specific
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project combines techniques from machine learning, natural language processing (NLP), and knowledge representation to support legal scholarship and decision-making. The position entails close academic
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Qualifications PhD in epidemiology, biostatistics, data science or comparable; Ideally, an experience working with insurance claims data; Good writing skills in English. Interest in working in an international and
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natural and highly polluted conditions, and how these processes can be represented in models to theoretically assess the potential of cirrus cloud thinning as a climate intervention strategy. The selected
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or computational linguistics focused on natural language processing. Two to five years of experience is required. Experience in the civil service would be appreciated. Solid experience with neural language models
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letter Writing sample (e.g., PhD thesis) Copies of diplomas Contact details of three academic referees Early application is highly encouraged, as the applications will be processed upon reception. Please
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Biological sciences » Biological engineering Chemistry » Applied chemistry Chemistry » Biochemistry Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 1
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30 Sep 2025 Job Information Organisation/Company Université Grenoble Alpes Research Field Computer science » Computer systems Researcher Profile First Stage Researcher (R1) Country France
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the Job related to staff position within a Research Infrastructure? No Offer Description The LIG brings together nearly 450 researchers, lecturers, PhD students and research support staff. They belong