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experience from which comparable knowledge is demonstrated may be considered. Preferred Qualifications Ph.D. degree. Demonstrated expertise in computational biophysics and molecular dynamics (algorithms and
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on developing innovative algorithms and models to address complex problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs
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on developing innovative algorithms and models to address complex problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs
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neuroscience, physics, or related fields is required. Minimum 1-3 years of experience in Monte Carlo simulations. Strong programming skills in Python. Proficiency with Monte Carlo algorithms, reaction-diffusion
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on developing innovative algorithms and models to address complex problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs
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on developing innovative algorithms and models to address sophisticated problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs
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on developing innovative algorithms and models to address sophisticated problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs
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checks that comply with IRB/ethical standards. Design and implement computer-vision algorithms. Develop, test, and refine deep-learning models (e.g., detection, segmentation, tracking) in PyTorch
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We are seeking a part-time Research Assistant in computer vision and machine learning for human behavior analysis. The successful candidate will investigate new algorithms and models for analyzing
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Responsibilities: Assist in research and development of computer vision-based tools and prototypes. Implement and test algorithms using OpenCV and related libraries. Design and iterate on intuitive and efficient