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Field
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of disease-related biomarkers, and study of organ-on-a-chip. We develop microfluid, optical and electrochemical methods in combination with microorganisms and cells to provide solutions for environmental and
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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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solving potential challenges in data integration, model performance, and interpretation; Preparing numerical and graphical summaries (visualizations) using relevant software and libraries for dissemination
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experience with computational modelling, method development, and numerical analysis applied to biology (or allied fields like neuroscience and medicine) the ability to work effectively in a multi-disciplinary
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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/PhD in a mathematical branch of study Solid knowledge of one of the following fields: mathematical methods, analysis and numerics in imaging, inverse problems or data sciences Ability to integrate
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Are you interested in developing screening methods to enable subsurface H2 storage? If you enjoy working at the interface between geoscience and engineering then this project may be right for you
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underground infrastructure (sensors, passive seismic methods, etc.) and surface impacts (e.g., satellite interferometry), (v) Predictive modeling of coupled processes (e.g., reactive transport, water-rock
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begin the process of appraisal of applications, taking into account the criteria set out in this notice. 5 — Evaluation and selection: 5.1 —The selection methods are curricular evaluation and an interview
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-growing academic health centers in the nation, Texas A&M Health encompasses five colleges and numerous centers and institutes working together to improve health through transformative education, innovative