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Field
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and iteratively improved. • Integrate and test autonomy stacks (perception, learning, planning) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain
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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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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 5 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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measurements in spine surgery, metabolomic/genomic factors on pre-optimization and post-surgical outcomes, and various clinical and radiographic factors associated with surgeries for adult degenerative and
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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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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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execute experiments that advance the frontier of self-optimizing microscopy, including automated alignment, adaptive focusing, drift correction, and AI-assisted atomic structure recognition. The role
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agentic orchestration frameworks for multi-step, multi-instrument experimental workflows (e.g., observe–reason–plan–act). Design closed-loop optimization and active learning strategies for real-time
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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