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. The position combines Empa UESL’s expertise in developing and accessing energy system models with the methodological expertise of the IMOS Laboratory in machine learning and foundation models. Postdoctoral
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
, Competencies, and Experience Strong research background in computer vision and multimodal machine learning. Experience with deep learning and multimodal/vision-language models. Strong programming and research
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, computer vision, robotics, biomedical engineering, computer science, biomechanics, neuroscience, signal processing, or a closely related discipline. Strong expertise in machine learning and deep learning
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Postdoctoral Associate Cornell Lab of Ornithology College of Agriculture & Life Sciences Cornell University Ithaca NY The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose
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(payable 14 times per year) Responsibilities The applicant is expected to establish an own research group with focus on advanced machine learning and deep learning techniques for remote sensing applications
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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 8 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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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution