57 data-analytics-phd "https:" "UCL" Postdoctoral positions at Oak Ridge National Laboratory
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Laboratory (ORNL). As part of our team, you will investigate the atomic and electronic structures in energy and quantum materials and correlate them with relevant properties for energy and data storage
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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, industrial engineering, electrical engineering, environmental
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materials. In this role, you will develop and apply methods that integrate physics‑guided image correction with intelligent (AI/ML‑enabled) data‑acquisition strategies. Key objectives include (1) implementing
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/ML techniques for real-time, real-world data Transport and dispersion modeling Fate modeling of materials in the atmosphere Applied statistics Data analytics Deliver ORNL’s mission by aligning
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manufacturing competitiveness analysis relevant to advanced manufacturing technologies. The team builds and applies analysis tools and analytics to draw insights on the manufacturing sector’s impact on energy
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will involve designing beam dynamics experiments, measurement, simulation, and data analysis. This position resides in the Accelerator Physics Group in the Accelerator Science and Technology Section
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ferroelectric and ferroelastic materials, under external stimuli such as electric fields, light, strain, and temperature. This position resides in the Data Nanonanalytics (DNA) Group within the Nanomaterials
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techniques; and (3) developing advanced methods for inelastic neutron scattering data analysis and workflow automation. The postdoctoral researcher will work in close collaboration with Dr. Raphaël Hermann and
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affordability of energy supplies. The PSR group focuses on resilience, data analytics, protection, and EMT simulation research. Major Duties/Responsibilities: Develop electromagnetic transient (EMT) models
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data analytics using tools in programming languages such as Python, PyTorch, Pandas, Scikit Learn, etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient