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Field
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-centered learning environment. We aspire to develop leadership skills in our students and a strong commitment to their professions, to interprofessional collaboration, and to society. These efforts
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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data
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proposed mentors from current faculty associated with the Kempner Institute or working on machine learning, artificial intelligence, or computational neurobiology at Harvard. A research proposal of no
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., • Interest in developing risk prediction models via deep learning/machine learning. • Have strong background in DL, EEG data and programming for the implementation of proposed methods. Apply now
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with physical objects/environments, and audio rather than video based AR can help enhance memory/learning Humanizing Computer Mediated Communication: Synthesizing co-presence - What does it mean to feel
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or machine learning to complex data. The successful candidate will have (or be nearing completion of) a PhD in a relevant field such as polymer science, materials engineering, or mechanical engineering
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with a PhD in computer science or bioinformatics are encouraged to apply. We create statistical, machine learning, and deep learning approaches for the processing of this data, with a major focus on
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in image processing, quantitative analysis, and biological interpretation Proficiency in AI/machine learning tools for image segmentation, transformation, registration, or tracking Solid mathematical
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life insurance, generous paid leave and retirement programs. To learn more about UofSC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Research Grant
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and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In addition to your research leadership, you will play a