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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 month ago
and papers supporting the scientific case for polarized deuterons. Contribute to the design, simulation, and performance optimization of the BHCal for the EIC. Participate in the assembly, calibration
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from telescope data. The design of robust uncertainty quantification tools is a core component of this effort. -On the experiment design side, the group develops simulation and optimization algorithms
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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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, and method development skills on protein extraction optimization, fractionation, purification, characterization, binding properties, modification, and application in food systems. Also a sloid knowledge
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for the fundamental understanding and engineering design of safe autonomy for autonomous vehicles. The post-doctoral researcher should have a strong background in autonomous vehicles, optimal control, differential
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, and maintain high work ethics. We especially welcome applicants who are recent graduates and are interested in pursuing an independent academic career. Job Duties Designs, optimizes, and executes
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are available. Appointments cannot exceed five years. KEY RESPONSIBILITIES: Synthesize and optimize CNS-targeted ligands and radiolabeled compounds. Develop PET tracers for neuroreceptors, enzymes
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the city we call home, UB is distinguished by a culture of resilient optimism, resourceful thinking and pragmatic dreaming that enables us to reach others every day. Visit our website to learn more about the
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Internal Number: JR89988 Position Summary This position will primarily support the implementation of a newly funded National Institute of Mental Health R01 grant focused on optimizing a digital chatbot
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resonance imaging (MRI) experiments in living rodent models to complete the work proposed in funded grants (CPRIT, NIH, other grants). Develops and optimizes scan sequences to enable quantitative in vivo MRI