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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. What We Offer As an employer, we
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the intention of the subject. The advancement in brain decoding models benefits the development of brain-machine interfaces, Neuroprosthetics and the understanding of neurological disorders such as
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23.02.2026 Application deadline : 30.04.2026 The Autonomous Systems Lab at the University of Tübingen is searching for a Postdoctoral Researcher in machine learning (m/f/d, E13 TV-L, 100%) limited
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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a
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transportation systems and autonomous driving. • Strong understanding of generative AI, deep learning, and multimodal machine learning, with hands-on experience. • Excellent programming skills and proficiency with
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energy consumption in information processing and machine learning (e.g., arXiv:2308.15905); Quantum phenomena in information processing: exploring how quantum effects can be utilized to process information
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 01-Apr-26 Location: Boston, Massachusetts Type: Full-time Categories: Academic/Faculty Computer/Information Sciences
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Assistant Professor - Information Systems, Operations Management, Supply Chain Management, and Busin
AI/ML methodologies , advanced analytics , and data-driven decision making to solve contemporary business challenges. Research methods of particular interest include machine learning , deep learning
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and growing scientific team, with opportunities to shape the direction of an exciting research program at the intersection of gene regulation, stem cell biology, and machine learning. This is an
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the following is desired: - Agentic and sequential decision-making for autonomous experimentation, including active learning and optimal experimental design - Generative and probabilistic modeling