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Field
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on multiple components by analyzing maintenance data, performing failure analysis and giving recommendations backed by laboratory testing and emulation results. The candidate must be comfortable with operating
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noise from mechanical/vibratory sources. Two methodologies for monitoring the state of health of a structure based on an experimental modal analysis of the structure have been proposed: - the detection
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for strategic development: materials research, integration of batteries with power electronics, and safe energy storage. This position is associated with the COMPEL platform located at the Centre for Analysis and
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diseases including pulmonary hypertension, and 2) identifying novel precision therapeutic approaches and targets for lung diseases and right heart failure due to pulmonary hypertension. Current research
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the immunological and non-immunological mechanisms of late graft failure. Work will include collection, analysis, and interpretation of data, presentation at professional meetings, manuscript preparation, and grant
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measurements at the Center for Nanoscale Materials and analysis of these data. The in-situ experiments will be driven by external electric fields and relevant optical stimuli. Although exceptional candidates in
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candidate will conduct research related to a wide range of topic areas including the analysis of energy and power systems, optimization of veriable energy resources, electricity market design and operation
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, gene expression analysis by qRT-PCR and immunoblot, lipid extraction and analysis using HPLC, analysis of datasets generated by bulk or single-cell RNA sequencing. Salary will be commensurate with NIH
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techniques, and carry out any required data analysis. Position Requirements • Experience working with Lorentz Transmission Electron Microscopy. • Strong background in Materials Science or Physics. • Min
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for bioprocessing applications. The position will include designing automated experimental systems, electrode synthesis or surface modification, and integration of experimental findings with system-level analysis