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Field
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cross-regional logistics network scenarios, data collection and development of decision support models for optimal maritime infrastructure for collection of the captured CO2 from the emitters and
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? No Offer Description Work Plan: The objective of the work is to optimize PHA production from fruit waste and sludge from wastewater treatment plants (WWTP) using mixed microbial cultures in a four
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appointment). Strong background in statistical or machine learning methodology, optimization, or high-dimensional data analysis. Proficiency in R or Python; experience with deep learning, causal inference
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XLZD. XENONnT is a dual-phase liquid xenon TPC at LNGS with a 5.9-tonne active mass, optimized for WIMP detection with sensitivity to spin-independent cross sections down to ~10⁻⁴⁸ cm². Its low
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to candidates with knowledge and skills in optical communications and signal processing for communications. Work Objectives: The objective of this project is to develop: 1) optimized pre-amplification schemes
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years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
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include the analysis of VOCs emitted from plants to understand optimal conditions for controlled environment agriculture (CEA). By tailoring ‘light recipes’, CEA can optimise plant growth, enhance
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University of North Carolina at Greensboro | Greensboro, Indiana | United States | about 17 hours ago
The Postdoctoral Fellow is expected to work both independently and in close collaboration with the Principal Investigator (PI) Dr. Ayalew Osena and students to optimize Agrobacterium transformation and improve
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objectives to be achieved: The work plan includes numerical modeling of the injection modeling process using commercial software, and the integration of these programs with AI-based optimization methodologies
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and present results at peer-reviewed venues and conferences. Example project areas Performance analysis and optimization of end-to-end scientific workflows, including those originating at DOE facilities