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California State University, San Bernardino | San Bernardino, California | United States | about 14 hours ago
advanced AI/LLM technologies. The ideal student will have basic experience in the following: programming skills, AI/LLM models, and data analysis and visualization. Responsibilities include but not limited
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assessment of physical, chemical, and, where relevant, biological soil properties. Additional tasks include data processing, analysis and documentation, as well as supporting the modelling of soil
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randomisation systems. Your work will ensure data integrity and compliance with regulatory standards, enabling robust statistical analysis and impactful trial outcomes. You will be responsible for the set-up and
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), and next-generation compact sources. Research activities include the development of measurement techniques, experimental protocols, and data analysis methods that fully exploit the high brightness
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hotline. Conducts research and analysis on SCM, procurement, contract activity, supplier performance, stakeholder feedback, and operational trends. Gathers, validates, organizes, and interprets data
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environment (Essential). Working knowledge of relevant specialist analytical software used in the analysis of isotope datasets (Desirable). Strong data analysis skills, with the ability to gather, interpret
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environment (Essential). Working knowledge of relevant specialist analytical software used in the analysis of isotope datasets (Desirable). Strong data analysis skills, with the ability to gather, interpret
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friabilimeter, IEC mash bath, Anton Paar densitometer, UV-Vis Spectrophotometer, and Fisher Scientific Gallery analyzer Data collection, organization, maintenance, analysis and reporting, including upkeep
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techno-economic analysis (TEA) and life cycle assessments (LCA) of relevant hydrogen and low-carbon technologies Evaluate the technical feasibility, commercial viability, and scalability of emerging
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modeling and genomics data analysis. This is an excellent opportunity for a recent graduate with a strong quantitative background to gain hands-on research experience at the intersection of computational