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Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description The Machine Learning for Integrative Genomics team (https
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We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a one-year Postdoctoral position. Using recent
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for 10-12 weeks. Responsibilities: Collect and organize different datasets. Derive summary statistics of those datasets. Help with the implementation of machine learning models. Conduct literature
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of computational methods that enable machines to perform tasks requiring perception, learning, reasoning, and decision-making. It encompasses core areas such as machine learning, data-driven modeling, intelligent
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an increased interest in adapting and developing the latest machine learning methods for the purpose of malware detection, and preliminary results are encouraging. The specific goals of this project include
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group discussions. Assist faculty during the semester. Assist in assigning work that develops the student's understanding of critical thinking. Assist students in writing and learning the material
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family-friendly and cultural programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer committed to providing
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Develop instrumentation and fixtures for the automation. Capabilities in advanced finite element or machine learning tools for process optimisation Disseminate the research outcomes into Journal
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 17 days 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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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models