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sources of ionizing and UV radiation to ice samples doped with biogenic materials. Experiments include (i) those in which candidate biosignatures are mixed and frozen to simulate accreted sub-surface ices
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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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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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)-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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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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, imaging informatics, and large language models (LLM) is preferred. This position will require collaboration with multiple stakeholders, including informatics experts, clinicians, basic scientists, and
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development for data interoperability and system integration. Experience working with digital twin platforms, simulation environments, or real-time sensor data. Prior contributions to research software, open
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, evo-devo, neurophysiology, and behavior projects ongoing. Work will primarily be related to the genetics and physiology of opsin-expressing sensory cells in the lab’s model system, Nematostella
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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
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neurodegenerative diseases and iPSC-derived neurons representative of the disease. Testing promising compounds for their in vivo pharmacokinetic properties in wildtype and mouse models for neurodegenerative diseases