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Field
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions
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technology Technology » Dating techniques Technology » Information technology Researcher Profile Recognised Researcher (R2) Application Deadline 31 Aug 2026 - 23:59 (UTC) Country Belgium Type of Contract To be
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- centric AI systems. Assistive / Collaborative Robotics: Interest in developing robotic systems for rehabilitation, assistive technology, or neuro-prosthetics, leveraging machine learning to improve
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and Technical Acoustics is looking for a postdoc for the Swedish Research Council-funded SMILE project (Soundscape engineering with Metamaterial Innovation and LEarning). SMILE develops acoustic design
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computing and artificial intelligence. Areas of interest include, but are not limited to, the following: Quantum Machine Learning and AI: Develop novel quantum algorithms and computational frameworks
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-physical systems, digital twins, BIM, AI and machine learning, robotics, or digital construction technologies is highly desirable. Strong programming and analytical skills, excellent written and verbal
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 21 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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, contributing to research proposals, and supporting the development of externally funded, multi-institutional projects. Required Minimum Qualifications: Ph.D. in Mechanical Engineering, Computer Engineering
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, distributed control, formation control, perception, and decision-making in complex environments. Design and implement data-driven approaches, including machine learning, reinforcement learning, surrogate models