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NLP/LLM Postdoctoral Research Fellow

Full Time

Massachusetts General Hospital

About the Role

The Massachusetts General Hospital ICU Precision Medicine Lab, as part of the MGH Clinical Data Center and the National Patient-Centered CHoRUS for Equitable AI Collaboration, is seeking a Postdoctoral Fellow to participate in ongoing research studies focused on using NLP/LLM and machine learning and classification to develop methods for recognizing complex patient states for defining outcomes and performing risk prediction that have greater value compared to individual clinical parameters.

We are seeking a highly motivated Postdoctoral fellow who will have the opportunity to work with multi-year wide and deep data sets containing electronic health records, social determinants of health, and unstructured text data.

Primary responsibilities include extracting meaning from the large corpus of enterprise medical data from clinical data as above. The incumbent should have a strong background in computer science, statistics, applied mathematics, or related fields with an outstanding track record. In addition, the applicant should have excellent programming skills in MATLAB and Python and potentially have emerging experience with machine learning, neural networks, or deep learning frameworks. The postdoctoral fellow will work with the PI and other members of the laboratory as well as collaborators to ensure smooth and successful outcomes for this research.


Research Design and Implementation

·         Develop and execute research projects focused on the integration of Large Language Models (LLMs), Natural Language Processing (NLP), and electronic health record (EHR) data in healthcare settings.

·         Design and implement experimental methodologies for data collection, analysis, and interpretation.

·         Algorithm Development and NLP Applications:

·         Collaborate with interdisciplinary teams to design and develop NLP algorithms for extracting meaningful information from electronic health records.

·         Apply LLMs to enhance NLP capabilities, improving the understanding and utilization of healthcare data.

Data Processing and Analysis

·         Preprocess and analyze large-scale electronic health record datasets, ensuring data quality, and extracting relevant clinical information.

·         Apply statistical and machine learning methods to derive insights from healthcare data.

Clinical Informatics Collaboration

·         Collaborate with clinical informaticians and healthcare professionals to understand the specific requirements and challenges in implementing LLMs and NLP in real-world healthcare environments.

·         Integrate research findings into clinical informatics applications for improved healthcare decision-making.

Literature Review and Synthesis

·         Conduct comprehensive literature reviews on relevant topics in LLMs, NLP, and electronic health record data in healthcare.

·         Synthesize and critically analyze existing research to identify gaps and opportunities for contribution.

·         Publication and Dissemination:

·         Prepare research findings for publication in peer-reviewed journals and present at conferences, disseminating knowledge in the field.

·         Collaborate with team members to contribute to conference abstracts and research posters.

Grant Proposal Development

·         Assist in the development of grant proposals to secure funding for ongoing and future research projects.

·         Provide expertise in LLMs, NLP, and electronic health record data to strengthen grant applications.

·         Interdisciplinary Collaboration:

·         Collaborate with researchers, healthcare professionals, and informaticians in interdisciplinary projects.

·         Attend and actively contribute to research meetings, workshops, and seminars.

Technology Proficiency

·         Stay current with advancements in LLMs, NLP, and healthcare informatics technologies.

·         Demonstrate proficiency in relevant programming languages (e.g., Python) and frameworks.

Ethical Considerations in Healthcare Data Research

·         Adhere to ethical standards in research, ensuring privacy and security of patient health data.

·         Stay informed about ethical challenges and considerations in the intersection of healthcare, LLMs, and NLP.

Documentation and Reporting

·         Maintain detailed documentation of research activities, methodologies, and results.

·         Provide regular progress reports to project leaders, collaborators, and funding agencies.

·         Teaching and Mentorship:

·         Potentially contribute to teaching activities related to LLMs, NLP, and healthcare informatics.

·         Provide mentorship and guidance to graduate and undergraduate students involved in related research projects.


·         Actively engage in the academic and healthcare research community, building professional relationships.

·         Attend conferences, seminars, and networking events to establish connections in the field of LLMs, NLP, and electronic health record data in healthcare.

The MGH Center for Neurotechnology and Neurorecovery (CNTR) develops, tests, and deploys novel neurotechnologies to improve the care of people suffering from diseases or injuries of the nervous system.

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