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Field
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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. Responsible and dependable with attention to deadlines and skills in time management. Motivated, creative, and ready to learn new things. Skill in handling multiple competing priorities. Specialized skills in
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Proteomic and metabolomics analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job
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focus on machine learning in the Stanford Center Cancer Cell Therapy at Stanford University School of Medicine. We seek a highly creative and motivated scientist to perform cutting-edge computational
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d) excellent written, oral communication skills e) strong data analysis skills. Ideal applicants will also have experience with some combination of: a) Machine learning e) code optimization and
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materials such as the magnetoelectric, high entropy oxides, through neutron scattering experiments. Additionally, collaborative work will be performed with the aim of developing and applying machine learning
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD degree (or foreign equivalent) in Electrical Engineering or a closely related field in hand by
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career in research or academia. Participate in the University’s Individual Development Plan policy for postdoctoral scholars, have a PhD or equivalent terminal degree (such as MD, DVM, PsyD, PharmD, etc
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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, and machine learning, the program prepares graduates to drive progress in fields like renewable energy, 5G, cyber-physical systems, and smart infrastructure. University Information The University