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Field
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Computer Vision and Generative AI. The successful candidate will join a dynamic research environment and contribute to cutting-edge research at the intersection of computer vision, generative artificial
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understand cancer biology, identify diagnostic and prognostic biomarkers, and improve cancer therapy. Projects will involve the development of AI solutions, particularly by leveraging large language and vision
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Vision and speech processing, multimodal signals processing and fusion; Proficient programming experience in Python and libraries (e.g., Pytorch, TensorFlow) with several years of practice; First-author
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QUALIFICATIONS: • PhD in Electrical and Computer Engineering, Mechanical Engineering, Physics, or a closely related field. • Demonstrated expertise in MEMS/NEMS design and modeling, including finite element
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structural topology optimization vision-based structural health monitoring autonomous and drone-based infrastructure inspection and vibration monitoring Successful candidates must hold a PhD degree in Civil
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. In particular, he/she will be expected to :• Select and evaluate the most suitable approaches from the wide range of machine learning and computer vision methods available in the literature, with
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, validation, deployment, monitoring, and (preferably) MLOps practices. You can develop and integrate AI models into operational systems, including: user‑facing applications (computer vision, sensor fusion
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. Required Qualifications Education: PhD , MD or equivalent. This position requires a formal degree in the cited discipline area(s) to ensure that candidates have advanced knowledge, analytical skills and
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/On Overview RUTGERS CANCER INSTITUTE VISION, MISSION, AND CORE VALUES: VISION: Through our science, our evidence-based actions, our openness, and our respect for those we serve, we will advance cancer-focused
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analyses. Machine learning for biological data (e.g., protein language models, transformers, generative models) and interest in building interpretable tools for experimental colleagues. Qualifications PhD