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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 11 days ago
include advancing foliar trait retrieval algorithms, atmospheric correction over dense humid tropical canopies, machine learning and dimensionality reduction for handling big data, landscape evolution
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machine learning and quantum information theory. The position focuses on theory but can potentially collaborate with experiments closely. The detailed topics can be, but not limited to, quantum metrology
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, Statistics, Computer Science or conjugate subject and have a strong record of publication in the relevant literature. Good knowledge of machine learning algorithms is essential, as well as proven competence in
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ethic, and enthusiasm for learning as much as prior technical expertise. Note: Candidates with complementary training backgrounds are encouraged to apply if they bring strong motivation to learn new
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investigators. Biostatistics methods will include complex survival analysis, longitudinal analysis, machine learning, and causal inference. You will benefit from access to unique datasets and expertise from two
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, and biological signals to construct coherent models of microbial organization across scales. Success in this role will require creativity in computational imaging, machine learning, and signal
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environment. Must be able to manage multiple and rapidly changing priorities and learn new skills. Must be detail-oriented and well-organized and the ability to work in a team environment. Excellent verbal and
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neural recording and imaging modalities. Experience with signal processing, machine learning, statistical modeling, and multimodal data integration. Strong scientific writing skills, with a demonstrated
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IRCM - Cancer Research Institute of Montpellier | Montpellier, Languedoc Roussillon | France | 3 months ago
, Computer Vision, AI, Medical Imaging, or a related discipline. ● At least 3 years of research experience (post-PhD), ideally in an academic or public research setting. ● Solid grasp of deep learning theory
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation