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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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://isor.univie.ac.at/ ) of the University of Vienna is offering a PhD position in the area of Mathematical Statistics and Machine Learning, starting on 01 March 2027. The group's research is focused on the current
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Join Us: Information Strategy and Services (ISS) partners across the campus to deliver information technology services that empower teaching, learning, research, and patient care. ISS is organized
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flexible fitting of mechanistic and statistical models to the data. Our current focus is on generating datasets of sufficient size and diversity to train machine learning models to accurately predict how
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optimisation techniques, machine-learning methods and data-driven modelling. Familiarity with robotics and autonomous-system simulation environments such as ROS/ROS2, Gazebo, AirSim, Unity, or equivalent
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of health equity. Learn more: https://hospital.uillinois.edu/about-ui-health This position is intended to be eligible for benefits . This includes Health, Dental, Vision, Life Insurance, a Retirement Plan
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, machine learning or computational modelling. Familiarity with Linux, high-performance computing, version control and reproducible research practices. Previous research on language, reading, auditory
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that support advanced machine learning, reinforcement learning, and foundation model research across large-scale clinical environments. What You'll Do: Design, build, and optimize distributed data processing
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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