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                Field
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                Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical 
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                . The candidate is expected to: Advance cutting-edge knowledge on the electronic structure of spin-crossover systems with higher nuclearity Adquire advance knowledge in coordination chemistry Develop strong 
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                . Our research integrates clinical, neuroimaging, physiological, and immunological data to advance mechanistic models of severe mental illness. Relevant links: https://tmu.pure.elsevier.com/en/persons 
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                photosynthetic membranes. Specifically, pump-probe ultrafast nanoscopy of transport in model OPVs and light-harvesting complexes. Specifically, advance structured excitation methods for low light level exciton 
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                , or related field - Expertise in advanced quantitative/statistical methods (e.g., generative computational modeling, multilevel and/or structural equation modeling, univariate and/or multivariate fMRI 
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                (AI), mechanistic modeling, and quantitative systems pharmacology (QSP). Funded by an NIH R01, our group develops predictive frameworks that integrate advanced AI/ML methods with multiscale mechanistic 
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                electrogravimetric response. To do this, we will implement a multi-stage approach, combining the development of model electroactive materials, their physico-chemical characterisation, and the advanced exploitation 
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                quantitative systems pharmacology (QSP). Funded by an NIH R01, our group develops predictive frameworks that integrate advanced AI/ML methods with multiscale mechanistic models of disease biology and drug action 
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                supported by an individualized mentorship structure designed to foster scientific independence and career advancement. Prior trainees from this program have gone on to successful careers in both academia and 
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                candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage