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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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. Engage in a spectrum of research activities including literature review, experimental design and execution, data analysis, manuscript preparation, communications with collaborators and journal editors, and
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publish results in peer-reviewed journals. Training in bioethics, laboratory management, and scientific writing. Maintain accurate laboratory documentation of experiments, including raw experimental data
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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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, including literature review, experimental design, data analysis, collaboration, and dissemination of findings through conferences and publications. Apply for fellowships and awards, and provide mentorship
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innovative statistical methods in clinical trial design and variable selection methods in high dimensional data that will predict clinical outcomes and meta-analyses. The successful candidate will collaborate
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, excellent communication, data processing, organizational, written and oral presentation, and problem-solving skills. Experience in animal handling and/or high throughput sequencing analysis is strongly
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for diverse social and homeostatic states. The lab uses a combination of circuit-based strategies and custom engineered designs to investigate how social and non-social salient information is encoded in neural