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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 9 days ago
https://jobs.inria.fr/public/classic/en/offres/2026-09710 Requirements Skills/Qualifications Profile. We are looking for a candidate with: - M.Sc in Computer Science, Statistics, or related fields
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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 Materials science and technology are our passion. With our cutting
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that strengthen research outcomes. About You Qualifications: PhD awarded within 3 years in business administration, computer science, information systems, data science, biomedical engineering, or a related
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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job About the Job: This post-doc will work collaboratively in a multidisciplinary team of clinicians, scientists and engineers
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research program in immunology that advances the missions of the Department of Pathobiology, the College of Veterinary Medicine, and Auburn University. The active research strengths within the department
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the BERNARDO project, funded by the PEPR LUMA program (https://www.pepr-luma.fr/ ). The project focuses on chiroptical interactions, which play an essential role in fields such as biomolecular recognition
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computer labs, administrative computers and other technology hardware for Student Affairs units. Field Support Field support for workstations, printers and other peripheral hardware for residential computer
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candidates with strong backgrounds in computational neuroscience and machine learning. Applicants must hold a Ph.D. in Computer Science, Neuroscience, Biomedical Engineering, Applied Mathematics, or a closely
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/ . How to Apply: All applications should be made online via the above 'Apply' button. Under programme name, select Mechanical and Manufacturing Engineering. Please quote the advertised reference number: FP
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engineering, computational biology, biostatistics, or a related discipline, with a strong background in clinical data analysis and predictive modeling. Experience with multivariate regression techniques