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help develop new computational models that integrate molecular reaction networks with AI/ML algorithms in order to predict patient-specific cardiac remodeling and heart disease outcomes across human
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iteratively refined by converging evidence from downstream validation (such as chemo-genetics, structural modelling, functional assays, thermal proteome profiling and omni-omics), creating an adaptive and
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work has appeared as first- or co-first-author publications in Nature Cancer, Nature Genetics, and Cell Stem Cell, spanning statistical method development, cancer transcriptional regulation, and spatial
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(NLP) algorithms applied to electronic health records (EHR) to understand cannabis-related harms in aging PWH and people without HIV. The position will entail collaborations with several investigators
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simulations and data analysis. This includes data analysis using Python-based algorithms. Experience with EELS is preferred. Skills This See Experience
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languages like Python or C, and or developing and/or using computational methods for analyzing large datasets. Demonstrated experience in developing computational algorithms for solving problems, preferably
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importance in quantum materials research. While practical quantum applications typically require stable quantum bits with long coherence times, this research proposes innovative programmable algorithms
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experience in simulating/implementing quantum algorithms for field theories on quantum hardware. Appointment Detail: This post‑doctoral position is a full‑time, 12‑month appointment with annual renewal
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will contribute to high-impact projects, including: 1. Developing and validating algorithms that extract data from the Epic EHR (e.g., large language models) via comparison with manually extracted data
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typically require stable quantum bits with long coherence times, this research proposes innovative programmable algorithms that can harness the power of imperfect quantum simulators to tackle complex