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Simulation group to apply classical Molecular Dynamics and Machine Learning approaches for development of a new class of hybrid polyphenol-lipid nanoparticles with tuneable internal structure and exploration
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position under the supervision of Dr. Chris Smith. Our new lab in Evolution, Ecology, and Behavior investigates machine learning approaches for population genetics. Our research integrates custom neural
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of artificial intelligence and behavioral neuroscience. This individual will join our interdisciplinary team of computer scientists and behavioral neuroscientists seeking to identify the mechanistic and
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and/or digital culture). In addition to conducting their own research, supported by faculty mentorship, the Fellow will be expected to teach two courses per year: one lower-level course, such as
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- Life Sciences: Genomics, precision medicine, bioengineering, and health data science - AI and Digital: Machine learning, robotics, digital health, and cybersecurity - Defence and Advanced Manufacturing
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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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network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering Candidates should have demonstrated publication records in cardiac and vascular engineering or
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-cell microbiome analysis, or multi-omics data integration. Knowledge of machine learning approaches for resistome prediction or biomarker discovery is a plus. Why Join Us? Access to cutting-edge
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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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, leveraging advanced learning analytics, machine learning, and deep learning techniques. The candidate shall work under the supervision of the Principal Investigator (PI) and Co-PIs to conduct academic research