Postdoctoral Researcher - Biomedical Informatics and Data Science (BIDS)
- University of Florida
- Location: Gainesville, FL
- Job Number: 7308822
- Posting Date: Newly posted
- Application Deadline: Open Until Filled
Job Description
Job Description:
The Division of Biomedical Informatics and Data Science (BIDS) within the Department of Health Outcomes and Biomedical Informatics (HOBI) at the University of Florida (UF) is seeking a highly motivated and ambitious Postdoctoral Researcher with a strong background in Artificial Intelligence and Machine Learning (AI/ML). This is an exciting opportunity to join a dynamic and interdisciplinary research environment focused on developing innovative AI/ML solutions for healthcare and biomedical research. The successful candidate will join a collaborative research team to contribute to projects at the intersection of AI/ML, real-world data, and health outcomes. The Postdoctoral Researcher will play a key role in developing and applying cutting-edge methods to analyze large-scale clinica datasets, including electronic health records (EHR), medical imaging, and unstructured clinical text. The candidate is required to have a Ph.D. in biomedical informatics, computer science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM field. Prior working experience with EHR data, machine learning, deep learning, imaging informatics, and large language models (LLM) is preferred. This position will require collaboration with diverse stakeholders, including informatics experts, clinicians, basic scientists, and translational researchers in the department as well as across the University of Florida campus. In particular, the Postdoctoral Researcher will lead and assist with data modeling, AI/ML methods, software package development, scientific paper writing, and grant proposal writing.
More About This Role
Develop methods and tools to effectively use electronic health record (EHR) data; apply AI/ML methods to solve critical clinical and biomedical problems.
Implement methods into software that meets research needs, manage and update source codes as needed. Work in an interdisciplinary team of informaticists, programmers, information quality experts, statisticians, and researchers during software development.
Lead and participate in the design, implementation, and reporting of research and evaluation studies. Contributions to scientific reports, conference papers, journal articles, and grant proposals are expected, including documentation of method, presentation of data, and participation in interpretation of results.
Lead and participate in collaborative projects and contribute to data processing, software development, and other collaborative efforts.
Expected Salary:
$61,000-$64,000, commensurate with education and experience
Required Qualifications:
A Ph.D. in biomedical informatics, computer science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM fields.
Strong programming and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep learning, and data modeling techniques.
Preferred Qualifications:
Prior working experience with EHR data, machine learning, deep learning, imaging informatics, and large language models (LLM) is preferred.
Prior working experience with popular ML packages, e.g., PyTorch, Scikit-learn, TensorFlow, Pandas, Keras, NumPy, SciPy, etc., and traditional ML models such as support vector machines (SVMs) and Gradient Boosting Machines (GBM), as well as deep learning models (GNN, CNN, GCN, etc.) including LLMs.
Being able to develop informatics tools and software packages.
Excellent formal and interpersonal communication skills and the ability to communicate effectively to a diverse audience, such as students, clinicians, and PIs.
The successful candidate is expected to lead and contribute to research projects involving:
Utilization of real-world data (RWD) such as electronic health records (EHRs), medical images, and clinical notes in combination with AI/ML methods for health for biomedical and health outcome research.
Development and application of advanced informatics or AI/ML methods, including large language models (LLMs) and other foundation models.
Development and application of causal inference and discovery methods, particularly causal AI approaches for clinical trial emulation.
The Division of Biomedical Informatics and Data Science (BIDS) within the Department of Health Outcomes and Biomedical Informatics (HOBI) at the University of Florida (UF) is seeking a highly motivated and ambitious Postdoctoral Researcher with a strong background in Artificial Intelligence and Machine Learning (AI/ML). This is an exciting opportunity to join a dynamic and interdisciplinary research environment focused on developing innovative AI/ML solutions for healthcare and biomedical research. The successful candidate will join a collaborative research team to contribute to projects at the intersection of AI/ML, real-world data, and health outcomes. The Postdoctoral Researcher will play a key role in developing and applying cutting-edge methods to analyze large-scale clinica datasets, including electronic health records (EHR), medical imaging, and unstructured clinical text. The candidate is required to have a Ph.D. in biomedical informatics, computer science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM field. Prior working experience with EHR data, machine learning, deep learning, imaging informatics, and large language models (LLM) is preferred. This position will require collaboration with diverse stakeholders, including informatics experts, clinicians, basic scientists, and translational researchers in the department as well as across the University of Florida campus. In particular, the Postdoctoral Researcher will lead and assist with data modeling, AI/ML methods, software package development, scientific paper writing, and grant proposal writing.
More About This Role
Develop methods and tools to effectively use electronic health record (EHR) data; apply AI/ML methods to solve critical clinical and biomedical problems.
Implement methods into software that meets research needs, manage and update source codes as needed. Work in an interdisciplinary team of informaticists, programmers, information quality experts, statisticians, and researchers during software development.
Lead and participate in the design, implementation, and reporting of research and evaluation studies. Contributions to scientific reports, conference papers, journal articles, and grant proposals are expected, including documentation of method, presentation of data, and participation in interpretation of results.
Lead and participate in collaborative projects and contribute to data processing, software development, and other collaborative efforts.
Expected Salary:
$61,000-$64,000, commensurate with education and experience
Required Qualifications:
A Ph.D. in biomedical informatics, computer science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM fields.
Strong programming and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep learning, and data modeling techniques.
Preferred Qualifications:
Prior working experience with EHR data, machine learning, deep learning, imaging informatics, and large language models (LLM) is preferred.
Prior working experience with popular ML packages, e.g., PyTorch, Scikit-learn, TensorFlow, Pandas, Keras, NumPy, SciPy, etc., and traditional ML models such as support vector machines (SVMs) and Gradient Boosting Machines (GBM), as well as deep learning models (GNN, CNN, GCN, etc.) including LLMs.
Being able to develop informatics tools and software packages.
Excellent formal and interpersonal communication skills and the ability to communicate effectively to a diverse audience, such as students, clinicians, and PIs.
The successful candidate is expected to lead and contribute to research projects involving:
Utilization of real-world data (RWD) such as electronic health records (EHRs), medical images, and clinical notes in combination with AI/ML methods for health for biomedical and health outcome research.
Development and application of advanced informatics or AI/ML methods, including large language models (LLMs) and other foundation models.
Development and application of causal inference and discovery methods, particularly causal AI approaches for clinical trial emulation.
