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energy systems and demand response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems
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optimization. You enjoy hands‑on laboratory work, iterative troubleshooting, and mentoring junior researchers. You are collaborative, organised, and committed to producing high‑quality scientific outcomes. About
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Lehigh University Plasma Control Group (LU-PCG) and contribute to advanced control synthesis, neural observer development, scenario optimization, and AI-enabled digital twins for magnetically confined
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the effects of optimized gene expression regulation on plant performance under optimal and field-like conditions at the Netherlands Plant Eco-phenotyping Centre. Your research aims to improve tomato Zn and Fe
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Thin-film fabrication, processing optimization, and experimental troubleshooting Materials characterization using techniques such as GIWAXS, Raman, FTIR, UV-vis, and electrical transport measurements
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, liposomes and other vesicular systems. Proven experience in the design, fabrication and operation of microfluidic devices for the controlled production of soft materials. Strong background in polymersome self
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cuantitativo. Capacidad para trabajar con grandes conjuntos de datos y entornos computacionales. / Experience in data science, data management and optimization, and machine learning and AI techniques. Strong
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 11 days ago
use it to refine and optimize fabrication processes, establishing a tight feedback loop between detector behavior and process adjustments. Responsibilities may include: Developing and evaluating new MBE
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, optimization, and the experimental validation of communication-aware robotic systems. Position Requirements: Ph.D. in Robotics, Electrical Engineering, Telecommunications, Computer Engineering, Control
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. This position will focus on designing, optimizing, and implementing chemical biology and sequencing-based approaches to better understand how RNA structure and dynamics influence protein recognition