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(KCL, London, UK) but will also have the opportunity to travel and work at the Centre for AI and Machine Learning (ECU, Perth, AU) and the School of Psychiatry and Clinical Neuroscience (UWA, Perth, AU
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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, as well as the way memory, trauma and history are entangled. Job description: In close coordination with the project management and the other project members, the computer-assisted analysis
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 41 minutes ago
://doi.org/10.3389/fenvs.2025.1473890 Madani, N., et al., & Miller, C. E. (2024). A machine learning approach to produce a continuous solar-induced chlorophyll fluorescence dataset for understanding Arctic
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are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA
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be the continuation of previous work, would use machine learning on a simulated data base to define the tool, followed by an application to real data from GRAVITY/VLTI (K band), MATISSE/VLTI (L, M, N
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experience with software radios, 5G user equipment, VPNs, Tor, or other hard- and software relevant to the described systems an networks Up-to-date software development and machine learning skills, incl. work
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, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus