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learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP, BLIP) or scene-graph inference is a plus. Key Competencies Strong software development and debugging skills. Able
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has a fixed-term opportunity for a Postdoctoral Fellow / Senior Research Fellow to contribute to world-leading research in continual learning, computer vision, multimodal foundation models, and
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, procurement, or grant administration will be an added advantage. Experience in areas such as Machine Learning, Deep Learning, Computer Vision, Large Language Models, Data Analytics, or Intelligent Systems will
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operation and maintenance of equipment Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer
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processing, and sensor integration. Strong background in signal processing, computer vision, or machine learning. Proficient in programming languages such as Python, C++, and MATLAB. Strong publication record
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) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5, Franka Emika
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description NOMIS Foundation ETH Postdoctoral Fellowship The NOMIS
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. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under
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environments that aims to address how distributed sensing, fibre-optic monitoring, environmental observations, drone- and satellite-based data, operational infrastructure datasets, and/or machine learning can be
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, Computer Vision, or related areas, accompanied by enrolment in a doctoral programme in relevant areas, or by a commitment to submit proof of enrolment and registration by the scholarship contracting stage