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modern machine learning, with broad implications for both theory and applications. The PhD project focuses on the theoretical foundations and algorithmic design of discrete gener- ative models, situated
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization
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in this position is contingent upon adequate grant funds. Position Status Full Time Posting Number 26FA0741 Posting Open Date Posting Close Date Qualifications Minimum Education and Experience A PhD in
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. The QML-CVC team (https://qml.cvc.uab.es/ ) is part of the Computer Vision Center at the Universitat Autònoma de Barcelona; the CVC is a center with over 150 persons working machine learning and computer
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https://www.ubjobs.buffalo.edu/postings/64318 Employer State Faculty Appointment Term Position Type UUP Faculty Bargaining Unit 08 - UUP Posting Detail Information Fiscal Year 2026-2027 Position Summary
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technologies, and cloud platforms. Support researchers’ use of AI and machine learning tools, including AI coding assistants and agents (e.g., Claude Code), model APIs (e.g., Anthropic, OpenAI), and open-weight
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system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be preferred Good oral and written
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
into resources that can be used for machine learning. The PhD will therefore investigate multimodal approaches that connect visual sign-language information with textual representations under low-resource
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methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and scientific applications. About the research project We