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environment. The successful candidate will develop and apply advanced machine learning techniques—including multimodal AI, computer vision, and large language models—to complex scientific and engineering
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 22 hours ago
engineering or related fields. The ideal candidates will be rising sophomores or juniors with experience using Python or Artificial Intelligence (including Robotics, Computer Vision, Human Language Processing
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QUALIFICATIONS: • PhD in Electrical and Computer Engineering, Mechanical Engineering, Physics, or a closely related field. • Demonstrated expertise in MEMS/NEMS design and modeling, including finite element
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, artificial intelligence, computer vision, robotics, UAVs, etc. is a plus. Other preferred qualifications include: expertise in programming and coding (preferably using Python and C++) and GUI development
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description: African Sustainable Agriculture Research Institute (ASARI
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Researcher (R3) Application Deadline 26 Apr 2026 - 00:00 (UTC) Country Morocco Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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education and practical-based research with a vision of solving real African agriculture challenges by leveraging up-to-date science and technology. The AgroBiosciences Program Research Center leverages up
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for autonomous vehicle control and teleoperation applications. Expertise in the areas of vehicle dynamics, vehicle control, localization, computer vision, sensor fusion and estimation algorithms is desired
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of computational biotechnology and modelling/simulation of bioreactor under aerobic and/or anaerobic treatment of biomass. Candidates should hold a PhD degree with professional experience in biotechnology, process
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Spinoza's Theological-Political Treatise, this Schmidt Sciences-funded phase integrates large language models with computer vision to systematically uncover hidden networks of controversial printing during