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Field
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advanced machine learning methods for multimodal and 3D medical image analysis in musculoskeletal medicine, in close collaboration with clinicians and computer scientists. PhD or Postdoctoral Researcher
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for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins. There will be potential
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, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 43 minutes ago
of materials Proficiency in multi-physics FE simulations, topology optimization, and working knowledge of machine learning (ML) algorithms Demonstrated exceptional problem-solving abilities, a passion for
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international level Your profile PhD or equivalent qualification in computational biology, bioinformatics, or a related field in the computational or natural sciences Sound knowledge in machine learning
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the areas of Machine Learning (ML) and Artificial Intelligence (AI). Focus will be placed on applications of these technologies in cybersecurity and anomaly detection in encrypted network data. The successful
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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with SCION in New Zealand bringing together researchers in robotic perception, machine learning, remote sensing and silviculture to transform and upscale forest phenotyping operations. The role will be
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focused on the challenge of accelerating ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track