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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
research facility, the UNC Genome Sciences Building, ICS brings the best commercialization processes to the $1.15 billion UNC research enterprise. Position Summary We are seeking a postdoctoral research
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algorithms in the context of sparse optimization and applications in real world data sets. We are also interested in exploring opportunities for parallelism of the completion process, highlighting
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vivo experiments to investigate retinal neurovascular dynamics using the AO-RSO system; Developing or adapting high-speed image acquisition and processing methods for the simultaneous extraction
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: Sustainable mineral processing and hydrometallurgy, with emphasis on potash beneficiation, process optimization, and potash brine valorization. Main responsibilities: Conduct literature review on potash ore
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. To that aim, both Stein-based bilevel optimization, empirical Bayesian and unsupervised deep learning approaches will be considered. The recruited postdoc researcher will tackle both implementation challenges
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of autonomous flow reactors for chemical synthesis. The project aims at 1/ developing a new optimization Bayesian algorithm and 2/ improving the process-control software already developed in the team
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, both verbal and written, are required. Key duties: Successful candidate is expected to: Develop and optimize lipid extraction protocols Conduct lipid conversion into biodiesel Analyze lipid profiles and
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synthetic organic chemistry, including reaction design, optimization, purification, and structural characterization. Strong command of spectroscopic methods (H/C NMR, mass spectrometry, chromatography, etc
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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potentially involving techno-economic analysis and AI-driven models for optimizing design and operation. Activities within project management and co-supervision of graduate students are also foreseen