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Position Details Position Information Recruitment/Posting Title PostDoctoral Associate in Artificial Intelligence/Machine Learning and Data Science Department Quantitative Biomedicine Inst Salary
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 29 days ago
: Good programming skills, preferably in Python, and strong analytical abilities. A solid background in machine learning, computer security, privacy, distributed systems, or a closely related area
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of fungal communities, working with “big data”, and characterising soil physicochemical properties. You will join a diverse, active and collaborative research group in the Soil and Ecosystem Ecology Lab
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specifically of both hedonic and causal machine learning methods for modelling housing market values and familiarity with appropriate data sources to support these. Skills Essential C1 Ability to communicate
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno
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insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised
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Machine Learning within the School of Medicine at the University of Limerick. This is a methodologically focused PhD for candidates with strong quantitative backgrounds who wish to develop novel statistical