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developed and tested fall prevention interventions, examined the feasibility of digital health solutions such as apps, ingestible sensors, and wearables for medication adherence and vaccine uptake, and
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, perform data cleaning. Develop machine learning algorithms, natural language processing tools, and end user tools. Develop and interpret MCMC models; develop meta-models. Analyze epidemiologic and
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and wellbeing of older adults who seek emergency care. We have developed and tested fall prevention interventions, examined the feasibility of digital health solutions such as apps, ingestible sensors
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structured and unstructured biomedical data, including EHR data, wearable sensor time-series data, or clinical annotations. Demonstrated ability to build interactive or web-based systems using frameworks with
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working in the exciting and dynamic fields of deep learning and medical imaging. The main focus will be to develop novel deep learning algorithms to analyze medical imaging data (e.g., ophthalmology
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Bioinformatician at the intermediate or senior level. The Breuss laboratory is focused on employing and developing statistical machine learning, bioinformatic algorithms, and related approaches for the detection
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solutions to maximize recruitment and retention Serve as a resource and participate in study initiation and close out duties All ranks: Mark retinal images to develop AI algorithms for image biomarkers
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peer-reviewed journals. The Research Scientist works with large-scale datasets, conducts experiments to validate hypotheses, and refines algorithms for efficiency, scalability, and interpretability
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Responsibilities: Research (75%) Investigate literatures to identify existing solutions and the problem Develop and apply state-of-the-art natural language processing algorithms and models to multimodal data
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to build end-to-end diagnostic pipelines. Dynamic Imaging Analysis: Develop and optimize algorithms for time-resolved imaging data, including cardiac cine MRI and dynamic contrast-enhanced series