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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
detailed cost and emission analyses regarding production and processing of geologic hydrogen over a range of possible scales, depths, and compositions. Will regularly present research results at meetings and
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shortly after the application deadline. The applicants selected for assessment will be notified about the composition of the Assessment Committee and later in the process about the result of the assessment
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subduction processes. A key aspect of the research involves utilizing seismically constrained crustal rheology and structure to inform and validate our models. The incumbent will need excellent oral and
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modelling are an advantage. Documented experience in classroom teaching is a benefit. You will be driving the organic chemistry aspects of two projects. You will collaborate with research groups from
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chemical–biological pathway. The advertised positions will support these efforts through research on advanced carbon capture methods, process modelling and optimization, and biological CO2 valorization in
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subduction processes. A key aspect of the research involves utilizing seismically constrained crustal rheology and structure to inform and validate our models. The incumbent will need excellent oral and
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). The research project will develop a reliable framework to accelerate the development of novel high-power particle-production target material for advanced particle accelerator applications. A reverse modeling
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subduction processes. A key aspect of the research involves utilizing seismically constrained crustal rheology and structure to inform and validate our models. The incumbent will need excellent oral and
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characterization techniques with mechanical behavior and finite element methods. The postdoctoral candidate will develop the processes needed to connect mechanical testing data with 3D microstructure of nuclear
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computational models to map co-expression networks and predict systemic disease transitions. Characterise intestinal microbiome changes and their correlation with inflammatory diseases. Computational modelling