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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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dynamic research environments, exhaustive training opportunities and institutional collaborations. The PhD candidate will benefit from the computational resources available at CEPAM (GPU servers). He/She
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activities Present results in international conferences and workshops Your profile A PhD degree in Computer Science, Physics or a related field Strong background in understanding plasma physics and
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. This pluridisciplinary research team is led by Claudine Backes, PhD, Scientific Director of the National Cancer Registry of Luxembourg (Registre National du Cancer (RNC)) and head of the Cancer and Prevention Epidemiology
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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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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and
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ability to work in a cooperative, multi-cultural and multi-disciplinary environment. Dynamism, self-organization, autonomy and drive. Interest for computing biology (R programming, image analysis) will be
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; developing our partnership programme with industry; contributing to a quality management system; and the organization of webinars and other dissemination activities, including publications. Support from
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-class researchers from various fields, including neurophysiology, computational neuroscience, auditory cognition, genetics and genomics, cell biology and gene therapy. Most teams associate fundamental
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, computer science, population biology or a similarly quantitative discipline.