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metrology, ii) bottom-up synthesis of nanostructures, and iii) process engineering for energy and healthcare applications. Specifically, we leverage atomically thin (2D) materials as ideal model systems
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additional AD risk genes and to develop disease models. Work on the projects studying molecular mechanisms that recently identified Alzheimer’s disease risk repeat expansion variants contribute to disease
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clinical features using machine learning and foundational modeling approaches. This work supports disease modeling across chronic kidney disease, acute kidney injury, cancer, and neurological conditions. A
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influence placental physiology, fetal development and long-term health outcomes. The successful candidate will join a dedicated research team conducting translational studies using animal models, human tissue
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postdoctoral associate positions, starting immediately. Dr. Liu has extensive experience in big data analytics, systems biology, probabilistic graphical models, causal inference and machine learning. Dr. Liu's
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projects for this position include: (1) development of physiologically based pharmacokinetic (PBPK) and quantitative structure-activity relationship (QSAR) models of drugs, environmental chemicals, and
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)-statistics, (applied) mathematics, or a related STEM field. Prior working experience with EHR data, machine learning, NLP, bioinformatics, and large language models (LLM) is preferred. In particular
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function following cervical spinal cord injury (SCI). The position involves conducting preclinical studies using rodent models to explore neuroplasticity-based therapies—such as therapeutic acute
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as cell culture, gene editing, IP, WB, flow cytometry, etc. Epigenetics/Genomics approaches: ChIP, ChIP-seq/CUT&RUN, ATAC-seq, RNA-seq, CRISPR Screening, etc. Expertise in in vivo animal models
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collaboration with the UF Artificial Intelligence initiative. The successful candidate will have the opportunity to work on cutting-edge projects aimed at building large-scale models for neuroimaging and