103 parallel-and-distributed-computing-phd-"Meta"-"Meta" positions at Nature Careers in France
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at various levels (Bachelor, Master, PhD) Provide lectures in Theoretical Computer Science for bachelor and master programs Advise PhD candidates and contribute to doctoral and postdoctoral training (e.g
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Leveraging the spatio-temporal coherence of distributed fiber optic sensing data with Machine Learning methods on Riemannian manifolds Apply by sending an email directly to the supervisor
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-intensive PhD training programme, supported by the PRIDE funding scheme of the Luxembourg National Research Fund (FNR) and the programme's partner institutions: University of Luxembourg, Luxembourg Institute
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protein expression and purification, capable of producing thousands of proteins in parallel within weeks . 2) Eukaryotic expression systems facility for production of challenging protein targets. 3) A fully
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. Processing this response provides estimates of the local variations in acoustic pressure along the fiber, over distances ranging from 40km up to 140km with some systems. This technique, called Distributed
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, public authorities in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? We are seeking a PhD
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PhD degree in Computer Science, Physics or a related field Experience with parallel programming models Strong programming skills in C/C++ and/or Python Knowledge of distributed memory programming with
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of the biological bases of normal and pathological behaviors. Required Qualifications: PhD or equivalent experience in neuroscience, computer science, engineering or a related field. Proven experience in
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of Europe in the 20th and 21st centuries. It serves as a catalyst for innovative and creative scholarship and new forms of public dissemination. Your role Conduct a PhD Thesis Contribute to our dedicated
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Description This PhD project bridges computational neuroscience and machine learning to study the mechanisms of active forgetting—or unlearning—through the lens of both biological and artificial systems