Postdoctoral Associate

Classification Minimum Requirements The Division of Biomedical Informatics and Data Science within the Department of Health Outcomes and Biomedical Informatics (HOBI) at the University of Florida (UF) is seeking a highly motivated Postdoctoral Researcher with a strong background in biomedical informatics, artificial intelligence, machine learning, or data science. The successful candidate will join a collaborative and interdisciplinary research team to contribute to cutting-edge research at the intersection of AI/ML, real-world data, and health outcomes. Ongoing projects leverage large-scale multimodal clinical data, including electronic health records (EHRs), medical imaging, genomics, wearable devices, and unstructured clinical text, to develop novel methods for disease prediction, precision medicine, and clinical decision support. Applicants must have a Ph.D. in biomedical informatics, computer science, data science, biomedical engineering, statistics, or a related STEM field. Experience with EHR data, machine learning or deep learning, natural language processing, medical imaging, or large language models (LLMs) is highly desirable. Familiarity with causal inference, real-world evidence, multimodal data integration, or cloud/high-performance computing is a plus. Ph.D. in biomedical informatics, computer science, data science, biomedical engineering, statistics, biostatistics, or a related quantitative STEM field. Strong programming skills in Python and/or R; experience with SQL and Linux/Unix is desirable. Demonstrated research experience in biomedical informatics, artificial intelligence (AI), machine learning, or data science, including the analysis of large-scale biomedical or healthcare datasets (e.g., electronic health records, medical imaging, genomics, or other real-world data). Excellent written and oral communication skills. Ability to work effectively in a collaborative, multidisciplinary research environment. Classification Title Postdoctoral Associate Job Description Conduct independent and collaborative research in biomedical informatics, including the development and application of artificial intelligence (AI), machine learning (ML), and statistical methods to analyze large-scale, multimodal clinical data (e.g., electronic health records, medical imaging, genomics, wearable data, and unstructured clinical text). Design, implement, validate, and optimize computational methods and analytical pipelines to address clinically relevant research questions. Develop, implement, and maintain research software, data processing pipelines, and computational tools to support data integration, extraction, transformation, quality assurance, and analysis. Contribute to reproducible research practices, software documentation, and code sharing. Participate in the design, execution, analysis, and dissemination of research studies. Prepare scientific manuscripts, conference presentations, technical reports, and other scholarly products. Contribute to the interpretation of findings and collaborate with multidisciplinary teams on the publication of research results. Expected Salary $61,008 - $65,000 Required Qualifications Ph.D. in biomedical informatics, computer science, data science, biomedical engineering, statistics, biostatistics, or a related quantitative STEM field. Strong programming skills in Python and/or R; experience with SQL and Linux/Unix is desirable. Demonstrated research experience in biomedical informatics, artificial intelligence (AI), machine learning, or data science, including the analysis of large-scale biomedical or healthcare datasets (e.g., electronic health records, medical imaging, genomics, or other real-world data). Excellent written and oral communication skills. Ability to work effectively in a collaborative, multidisciplinary research environment. Preferred Experience working with electronic health records (EHRs), real-world data (RWD), clinical data warehouses, or other healthcare databases. Experience developing and applying machine learning, deep learning, natural language processing (NLP), computer vision, or large language models (LLMs) for biomedical or healthcare research. Experience integrating and analyzing multimodal data, including structured EHR data, clinical text, medical imaging, genomics, wearable devices, or other biomedical data sources. Familiarity with causal inference, statistical learning, clinical prediction modeling, or real-world evidence methodologies. Experience developing reproducible research software, computational pipelines, or open-source tools using modern software engineering practices (e.g., Git/GitHub, Docker, workflow management). Evidence of scholarly productivity through peer-reviewed publications and presentations at scientific conferences. Experience working on NIH-funded or other federally funded research projects and contributing to grant proposal development. #J-18808-Ljbffr