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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
characterization (XRD, SEM/TEM, Raman, spectroscopy, electrochemical analysis, etc.) Performs other related duties as assigned Required Qualifications Ph.D. in Materials Science, Engineering, Physics, Chemistry, or
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microscopy systems that integrate machine learning, robotic control, and real-time data analysis to achieve autonomous imaging and interpretation of complex materials systems. The Fellow will design and
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) and related disorders Analysis of behavioral data (e.g., assessment and treatment data; acoustic and linguistic analyses of connected speech), with potential to implement and/or learn brain imaging data
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-time data analysis, and tool-use APIs to automate complex decision-making across materials design, liquid-phase synthesis, and characterization platforms. Responsibilities include building agentic AI
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required by project needs Assist with program management, including IRB submission/modification, data management with RedCap, and data analysis Lead and co-author peer reviewed publications Support grant
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the various aspects of clinical and translational research projects, including study design and development, IRB submission, informed consent, data collection and analysis, abstract submission, data
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system dynamics and hydroclimate extremes. Strong programming skills including analysis of large hydroclimate datasets in Python or similar and creating analysis and visualization workflows on a
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and are also involved in planetary missions and climate modeling. These research projects produce large data sets and require computational analysis and visualization. This position is for one year with
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areas such as data analysis, statistical modeling, machine learning, numerical modeling, or remote sensing Preferred Qualifications A general understanding of ecosystem modeling or ocean circulation
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to candidates with knowledge of infectious disease epidemiology Responsibilities Lead the development and analysis of mathematical models to support the early detection, forecasting, and mitigation of emerging