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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 25 days ago
development of data processing tools. The position is for one year with the possibility of reappointment for an additional year. It will involve developing, implementing, and validating novel algorithms
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Requisition Id 15253 Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted
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magnetometry measurement and analysis of the Nab spectrometer magnetic fields, help develop the BL3 DAQ and algorithms, carry out Monte Carlo simulations for Nab and BL3, and help undergraduates at EKU finish
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research
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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with
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or accelerated acquisition and reconstruction algorithms will be highly valued. Instructions Interested candidates should apply via Interfolio link with their CV (including a full list of publications), a
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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans. Analyzes complex sensor data, works with a multidisciplinary team to develop health digital
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strong interest in machine learning and deep learning algorithms · Research and publication experience in health outcomes research Application Materials: · CV · Cover letter · Contact information for three
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune