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QUALIFICATIONS: • PhD in Electrical and Computer Engineering, Mechanical Engineering, Physics, or a closely related field. • Demonstrated expertise in MEMS/NEMS design and modeling, including finite element
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of biological and statistical physics. Candidates for the position must have a PhD in physics or a related discipline, preferably with expertise in stochastic processes, nonlinear dynamics, and control theory
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for these frameworks, and use them to analyze key performance indicators such as achievable rates, coverage, and energy efficiency, as well as optimize relevant system parameters. Applicants must hold a PhD degree or
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. Disseminate research through high-impact publications and conference presentations. Requirements: PhD (or near completion) in Imaging Science, Computational Biology, Bioinformatics, Machine Learning, Data
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, stem cell biology, and colon cancer. Position Status Full Time Posting Number 26FA0353 Posting Open Date 04/14/2026 Posting Close Date Qualifications Minimum Education and Experience PhD training is
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at international conferences. You hold a PhD in computational biology/chemistry, machine learning or a related quantitative field. You have a solid publication record and demonstrated experience with advanced
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, integrate device engineering with clinical workflows, and apply artificial intelligence and machine learning for automated image and signal analysis, tissue classification, and real-time diagnostics
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and/or in an R&D position, excluding time associated with family planning, military service, illness, or other life-changing events PHD must be completed prior to commencing employment This is a fully
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equipment (copy machine, fax machine, multi-line phone). Applications are being accepted on line at https://unmc.peopleadmin.com/postings/97497 Inquiries: David Warren, PhD at david.warren@unmc.edu Required
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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA