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Field
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models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems (e.g., transportation networks, manufacturing systems, and truck routing). Assessing
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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans for patients with chronic diseases. Analyzes complex sensor data, works with a
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proven ability to manage complex projects to completion on schedule. Excellent (written and verbal) proficiency in English, good communication and leadership skills. Additional Information Benefits A
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healthcare applications. We use large real-world complex datasets, including data extracted from electronic health records and medical images, for applications pertaining to patient diagnostics and prognostics
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analysis in medicine. Experience of software version control with Git, typesetting with LaTeX, use of Linux computers; Experience with graph-based methods, and graph convolutional/neural networks; Experience
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network excitability following traumatic brain injury using a variety of techniques, including in vivo whole-cell recording and two-photon microscopy, brain slice physiology, and genetic manipulation
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of modeling higher-order relational structures such as hypergraphs and simplicial complexes, which are prevalent in complex biological systems like protein assemblies, signaling pathways, and metabolic networks
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program) and a symbolic solver computing the solution. Another example is the generation of surrogate model architectures for complex industrial systems: the generative component proposes models based
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are Artificial Intelligence, Computational Science, and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational, and
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addressing complex challenges. Responsibilities will include writing reports, authoring publications, drafting patents, and presenting results at conferences. The ideal candidate will possess demonstrated