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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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. Proficiency in using PyTorch and other machine learning libraries for developing and implementing machine learning models is essential. Ability to prepare comprehensive written reports, including the use
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interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional neuroimaging data. Specific activities may include
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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for Optimisation and Machine Learning (QONOMics) project sits at the heart of this expansion. The project focuses on the physics of networks consisting of coupled harmonic and Kerr-nonlinear oscillators. By studying
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Informatics and edge intelligence etc. Must have documented significant Knowledge/Research Background, or Must be able to demonstrate skills on Data Analytics and Machine Learning, in particular on distributed
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of induced pluripotent stem cell-based human disease models 2) Understanding the role of immunity during tumorigenesis as well as disease relapses in order to design novel inhibitors, CAR-T cell therapy and
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 3 days ago
sensors (e.g., PPG); Testing medical device electronic interfaces; Applying signal processing and machine learning approaches to wearable sensor data (e.g., ECG, IMU, PPG); Developing computational modeling
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engineered constructs. Learn more about the innovative work led by Dr. Chris Chen here: https://bdc.bu.edu/bdc-team/. What you’ll do: Independently conduct research on liver cell proliferation, expansion, and
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2). Vascular network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering The Disease Biophysics Group is a creative, transdisciplinary group of engineers