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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Developing new machine learning
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language, most people also speak English. YOUR TASKS The Marchal lab (IDLab/IMEC Ghent University,https://idlab.ugent.be/people/802000961346) has an open PhD position within the Marie Sklodowska-Curie
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biology skills Experience with single-cell RNA-seq analysis Experience with machine learning based methods Have evidence of scientific accomplishment via peer-reviewed publications Understanding of cancer
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the design of antibody modalities Documented experience from cancer-related research Documented research experience in AI/machine learning/deep learning Documented experience in the development of therapies
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the use of machine learning and AI approaches • Integration of proteomics with genetic data via MR, coloc and FUSION to identify causal and druggable targets Requirements • The successful applicant will
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-front Physics , Living Systems , Machine Learning , many-body quantum geometry; altermagnetism; cavity quantum science; quantum non-equilibrium processes; Casimir physics , Material Science , Materials
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 month ago
skills Specific Requirements The candidate must hold a PhD in machine learning and have strong mathematical skills. Knowledge of bandit models is a plus. LanguagesFRENCHLevelBasic LanguagesENGLISHLevelGood
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. 3 or more years of demonstrable experience in machine learning theory. Excellent teamwork and communication skills Fluent in English The candidate who has obtained the highest score in the selection
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such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in
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project “Analytics for Learning with Machines” (ALMA) The position is TV-L E13, 75%, limited to 3 years, funded by the Deutsche Forschungsgemeinschaft (DFG). The project is a Franco-German collaboration