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mutations, etc.), analysis and integration of mass-spectrometry proteomics datasets, and artificial intelligence/machine learning (AI/ML) and systems-biology-focused efforts (i.e. large genomics and
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of Adjunct instructor to teach in the PhD program in the 2026/2027 academic year. This position, in conjunction with three other faculty, will teach a portion of the PhD course "Emerging Topics in Operations
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and experience: Qualifications PhD in Computer Science, AI, Machine Learning or related field Experience A strong research track record relative to opportunity, including the ability to produce and
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PhD in Computer Science, AI, Machine Learning or related field Experience Strong track record of publications in top-tier venues (e.g. CORE A*) Expertise in reinforcement learning, AI agents, and LLM
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Computer science » Computer systems Computer science » Programming Technology » Communication technology Technology » Telecommunications technology Researcher Profile First Stage Researcher (R1) Positions PhD
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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Millersville University of Pennsylvania | Millersville, Pennsylvania | United States | 21 minutes ago
Nature of position : Temporary, part-time Job Summary/Basic Function: The Department of English and World Languages is in need of temporary, part-time instructors to teach major courses and courses within
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inference, longitudinal methods, survival analysis, regression modeling, machine learning, or prediction modeling are required, as well as experience with statistical software such as R, SAS, or STATA. Prior
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assessment, programming and machine learning. If so, we encourage you to apply! You will develop exposure and physical vulnerability maps for past and future (1970-2100) and integrate these into a flood risk