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Field
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in digital health, fitness, and rehabilitation. Preferred Qualifications Background in machine learning/deep learning is a plus. Equipment Utilized Physical Demands and Work Environment PHYSICAL
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(e.g., deep learning, implicit neural representations, diffusion models) for CT reconstruction, enhancement, and defect detection. Advance algorithms for multi-modal tomography (X-ray, neutron, electron
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developing historical and future climate scenarios—including counterfactuals—and integrating them with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty
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. Required Qualifications: PhD in Computer Science, AI/ML, Computational Biology, or a related quantitative field. Proven expertise in deep generative modeling and large-scale multimodal learning. Experience
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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SIT's mission is centred on nurturing industry-ready graduates who possess deep technical expertise and transferable skills to address future challenges. We collaborate with industry in our
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research at an unprecedented scale. ROAR empowers educators, families, clinicians, and researchers with research-backed assessments to advance learning, accelerate research on learning differences, and
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positions are flexible in terms of research direction within 3D vision and graphics with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested in static and dynamic 3D
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 18 days ago
the ability to design and implement novel artificial intelligence algorithms. Examples of research directions could include: multimodal foundational models for biomedical data, deep-learning architectures
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for recording across multiple cortical/ striatal circuits •Optogenetic and electrical real-time perturbations, including interventions directly related to clinical deep brain stimulation. •Parametric