10 model-driven-engineering Postdoctoral positions at University of California Irvine
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systems/fabrication Biomedical computation/modeling Biomolecular and cellular engineering Biomaterials Tissue/regenerative engineering Neuroengineering Systems biology Cardiovascular engineering These
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Computer Science, Biomedical Engineering, Bioinformatics, or a related field. Required: Strong understanding of machine learning (ML) and deep learning (DL) methods, with hands-on experience in model development and
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Lab in the Department of Earth System Science at the University of California, Irvine seeks a Postdoctoral Scholar to lead an integrated remote sensing-modeling investigation of wildfire impacts and
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: The scholar will lead research projects linked to detecting large-scale ocean biogeochemical changes using a combination of remote sensing, modeling and in situ observations. Specifically, the postdoc will use
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modeling, artificial intelligence (AI), machine learning, and neuroinformatics methods to address pressing questions in clinical neurology. This position offers opportunities to work on large biomedical
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application) PhD, MD, or MD/PhD in biomedical engineering, physics, electrical engineering, or a related field Application Requirements Document requirements Curriculum Vitae - Your most recently updated C.V
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Science, Physics, Engineering, Biology or related fields by appointment start date. Completed applications must be submitted electronically through https://recruit.ap.uci.edu/apply/JPF09728 and must contain: (1
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experience with lasers, LEDS, optical system hardware, clean room fabrication, electronic circuit design, optical engineering, signal processing, and data analysis. Visit our website for more information about
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whose expertise lies in astroparticle physics, beyond the Standard Model physics, collider physics, cosmology, and neutrino physics. The theory group faculty members include Kevork Abazajian, Mu-Chun Chen
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using advanced bioinformatics and models. Specially, the postdoc will use our globally collected meta-omics data to understand controls, population dynamics, and ecosystem impact of marine microbiomes