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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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conduct research related to creating or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Basic Qualifications Core
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translate these discoveries into precision medicine. We develop computational and statistical methods while integrating human genetics, single-cell and multi-omics, large-scale biobank resources, and
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of funding. The start date is flexible and may be arranged by mutual agreement. The position is intended for a researcher interested in developing new theoretical and computational approaches to the strong
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epidemiology using mathematical, computational, and statistical modeling. We are seeking researchers who are passionate about understanding the mechanisms driving epidemic spread, designing mathematical models
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. Additional Qualifications: • Theory: Expertise in quantum optics, quantum information science, or quantum field theory. • Simulation: Proficiency in MATLAB or Python for simulation/modeling and data analysis
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the genetic and developmental basis of pediatric heart disease. We analyze genetic variation in samples from patients with congenital heart defects and utilize mouse, Xenopus and cell-based models to assess
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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bioinformatics and biomarker research. This position focuses on the integration and analysis of complex biological data to identify and validate kidney disease biomarkers. Research Focus Our program leverages
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven