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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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focus on technology foresight developing data-driven approaches for probabilistic modelling of new technologies; a second will focus on policy analysis leveraging machine-learning approaches
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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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Machine Learning & AI Spring semester: (beginning of January through mid-May) Python Programming for Data Science General Linear Models Deep Learning & AI The Tutor role will include the following
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a positive role model and build strong relationships with students, alumni, and the broader community. Computer and technological competency to support administrative and coaching responsibilities
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environment. Strong computer skills. 2:Superior written and oral communication skills. Ability to maintain strict confidentiality. Ability to deliver analysis with an appropriate balance of brevity and
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statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets. What we offer: • A stimulating
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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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to eligible team members. Learn more at https://hr.duke.edu/benefits/ Minimum Qualifications Education See job description for education requirements. Experience See job description for requirements. Degrees
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collaboratively within an interdisciplinary research environment. Desirable experience with advanced AI or machine-learning methods beyond standard predictive modelling prior exposure to qualitative or mixed