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Field
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astrophysics that can directly take raw, high-dimensional data from experiments or observatories and rapidly infer theory parameters, such as Higgs boson properties at the Large Hadron Collider or neutron star
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on reinforcement learning to optimize cooling and trapping parameters using real-time feedback for the Rydberg atom experimental platform. Enhance holographic methods for optical tweezers with generative models and
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the MHCG VCSELs. • Computer simulations of various versions of MHCG VCSELs in different material systems, • Designing the precise implantation parameters by using Stopping and Range of Ions in Matter
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tap density and material quality. Develop an advanced optimization and sensitivity analysis framework for accurate parameter estimation. Produce research publications in high-impact journals and
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/ ) – an European network of excellence in AI, Machine Learning (ML) and Computer Vision (CV), of which Vittorio is a Fellow member. For this particular position, the focus is on investigating AI approaches
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) for dynamic systems with unknown but measurable performance functions. Experience in system identification and online time-varying parameter estimation algorithms. Programming skills in MATLAB/Simulink, C/C
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to facilitate perceptual learning of different stimulation patterns; and (iii) the development of advanced AI algorithms capable of converting camera input into real-time electrical stimulation parameters. In
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
Hiring Range Dependent on experience and/or qualifications Proposed Start Date 03/01/2026 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in
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exploiting the broadband guidance handling capabilities of modern hollow fibers filled with gases. Such light source will allow exquisite control of key light parameters such as pulse durations, emission
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of the parameter space in relation to the statistical model. One of the main goals of SLT is to quantify the complexity of such models w.r.t. the data generating process (and some prior probability distribution