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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 3 hours ago
they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S. Citizens; U.S
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or Python. Responsibilities include teaching two undergraduate courses in each of Lawrence’s three ten-week terms. The successful candidate will be the third statistics and data science faculty member in
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platforms and our local CPU and GPU clusters; implementing Python tools for automating CSP/DFT calculations; - Participation in the scientific activities of the Applied Quantum Chemistry group (IC2MP) and the
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) - Signal processing and image analysis (Python) - Oral presentation of scientific results at meetings and international conferences. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5295
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Basilicata region. Where to apply Website http://www.unibas.it Requirements Additional Information Eligibility criteria Admission requirements: - PhD or equivalent degree obtained abroad; - Scientific and
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and modelers to use the optical constants in the analysis of observational data from, e.g., Hubble, Spitzer, JWST. Candidates are expected to be proficient doing data reduction and analysis using Python
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practice (e.g., Python/MATLAB/C++ and/or established modelling platforms). Familiarity with asymptotic and multiscale mathematical analysis methods to ground proof numerical simulations Strong communication
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acquisition, calibration, uncertainty/rigour and noise control). Strong signal processing skills (e.g., using Python and/or MATLAB). Ability to work effectively in an interdisciplinary team and communicate
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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vision and deep learning concepts, including object detection and image-based classification, with hands-on experience using Python and at least one deep learning framework (e.g., PyTorch or TensorFlow