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, fluorescence lifetime and vibrational imaging methods. Large-scale and high-throughput imaging and analysis pipelines: data acquisition and analysis to characterize cell-types and connectivity in mammalian brain
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biostatistical methods to better personalize treatment of cancer patients from deep and diverse types of biomedical information. Analysis of large‑scale clinical, genomic, and molecular datasets. Development and
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of the research, contributing to protocol writing, and helping to oversee the execution of the research. The postdoctoral scholar will also contribute to data analysis and write-up of scientific findings
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time of appointment. Demonstrated expertise in bioinformatics, statistics, microbiome data analysis, and/or computational methods. Experience working with large population-level datasets (e.g., NHANES
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discipline The ability to independently design and execute experiments and interpret data Expertise in at least one of the following areas: Super-resolution imaging, including Stimulated Emission Depletion
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transcriptomic data. • Detect and interpret structural variation from Nanopore/PacBio sequencing. • Build scalable, reproducible pipelines for large genome collections and public databases. • Collaborate closely
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biochemical datasets. A major focus will be on introducing new AI models to chart the chemical “dark matter” of the mammalian metabolome; examples of such models include large supervised or self-supervised AI
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Analyse and interpret time series of environmental monitoring data, and physical/ chemical parameters from tree rings Apply and further develop signal-processing and data-analysis methods Identify
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ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a multidisciplinary, highly
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traits. A NIDA-funded study concentrates on cannabis use genetics and PRS prediction of response to THC in a laboratory paradigm, and an NIMH study focuses on depression and anxiety genetics in large