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Field
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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
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, cardiac CT, ECG, portable ultrasound, and multimodal clinical data. Building scalable pipelines for image ingestion, DICOM/PACS interoperability, preprocessing, annotation, quality control, model training
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is expected to contribute to foundational research in dynamic modeling, stability analysis, and control of coupled data center–grid systems. Emphasis will be placed on developing new theoretical
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05.07.2026, Academic staff Our research combines mathematical modeling, numerical simulation, scientific computing, and data-driven methodologies to improve the predictive capabilities and
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that combines data from different molecular levels (transcripts, proteins, metabolites), aided by in silico target prediction, will enable us to uncover putative target of the compound, which will then be
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and AI-based approaches, including predictive modeling, stratification, explainable AI, and integrative multimodal analysis. The scholar will have opportunities to lead first-author publications
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Education: PhD in Chemical Engineering or related field Desired Experience: 3 years of prior research experience on multiscale modeling, hybrid modeling, and model-based control or related fields. Additional
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Education: PhD in Chemical Engineering or related field Desired Experience: 3 years of prior research experience on multiscale modeling, hybrid modeling, and model-based control or related fields. Additional
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; survival or hazard models; gradient-boosted decision trees or related predictive approaches; interpretable feature-importance methods; Git/GitHub; Linux or command-line workflows; preregistered analyses; and
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to develop predictive models for polymer-based materials. This project aims to leverage computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials