Postdoctoral Research Fellow in Clinical NLP and LLMs
Postdoctoral Research Fellow in Clinical NLP and LLMs
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Job Description
Mass General Brigham relies on a wide range of professionals including doctors nurses business people tech experts researchers and systems analysts to advance our mission. As a not-for-profit we support patient care research teaching and community service striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.
Job Summary
How to Apply:Applications will be reviewed on a rolling basis until the position is filled. For application submission and inquiries please contact:
When submitting your application please ensure the email subject line follows this format: NLP Postdoc Application Your Full Name
Join us in building trustworthy AI tools for ICU medicine and ALS research.
Interested candidates should submit a single PDF file including:
1. Two-page CV detailing relevant experience and publications.
2. One-page cover letter with exactly five bullet points each no more than two lines demonstrating your fit for this position.
3. Contact information for three references.
1. Two-page CV detailing relevant experience and publications.
2. One-page cover letter with exactly five bullet points each no more than two lines demonstrating your fit for this position.
3. Contact information for three references.
We at the Mass General Brigham NeuroAI Center are seeking a highly motivated Postdoctoral Research Fellow with expertise in machine learning (ML) and Natural Language Processing to contribute to cutting-edge research with real-world impact at the intersection of neuroscience critical care and computational modeling. This position will focus on developing advanced AI/ML models and frameworks robust finetuning retrieval-augmented generation (RAG) and structured extraction from noisy clinical transcripts and documents collaborating closely with clinicians and engineers.
Qualifications
How to Apply:
Applications will be reviewed on a rolling basis until the position is filled. For application submission and inquiries please contact:
When submitting your application please ensure the email subject line follows this format: NLP Postdoc Application Your Full Name
Join us in building trustworthy AI tools for ICU medicine and ALS research.
Interested candidates should submit a single PDF file including:
1. Two-page CV detailing relevant experience and publications.
2. One-page cover letter with exactly five bullet points each no more than two lines demonstrating your fit for this position.
3. Contact information for three references.
Key Responsibilities
Develop and fine-tune LLMs (LoRA/QLoRA) for ICU/ALS note classification temporal phenotyping summarization and structured JSON extraction (e.g. ventilator settings ALSFRS-R scores disease trajectories).
Build retrieval-augmented generation (RAG) pipelines (hybrid retrieval citation enforcement) with safety guardrails for generating evidence-grounded outputs from ICU/ALS corpora.
Mitigate hallucinations and rigorously evaluate robustness calibration and fairness across ICU subpopulations (age sex comorbidities) and ALS cohorts (site disease stage language).
Deliver reproducible pipelines with versioned data containers unit tests and transparent evaluation metrics.
Apply explainable AI (XAI) (concept attribution counterfactuals clinician-readable rationales) to enhance model interpretability in ICU monitoring and ALS progression modeling.
Collaborate with intensivists neurologists and data scientists to co-develop models aligned with real-world ICU workflows and ALS clinical research.
Contribute to manuscripts grant proposals and dissemination of findings at leading conferences and journals.
Ensure compliance with privacy regulations and participate in secure handling of sensitive ICU and ALS data.
Qualifications
Ph.D. in Computer Science Biomedical Engineering Computational Neuroscience Applied Mathematics or related field.
Strong expertise in machine learning and deep learning applied to clinical/biomedical data.
Proven experience with transformers LLMs and modern NLP frameworks.
Proficiency in Python PyTorch and related ML toolchains; experience with EHR/ICU note preprocessing and feature engineering.
Track record of publications in AI/ML for healthcare or neuroscience.
Strong problem-solving skills independence and collaborative mindset.
Excellent communication skills for both technical and clinical audiences.
Preferred Skills
Experience scaling experiments on cloud platforms (AWS GCP Azure) or HPC clusters.
Knowledge of self-supervised learning domain adaptation and federated learning for cross-site generalization.
Familiarity with ICU-specific challenges (e.g. predicting clinical deterioration sepsis ventilator weaning) or ALS research tasks (e.g. progression modeling survival prediction multimodal integration of speech EMR and imaging).
Familiarity with neurophysiological data (EEG telemetry) or neuroimaging is highly desirable.
What We Offer:
A dynamic interdisciplinary research environment at the forefront of AI in neuroscience and critical care.
Access to large-scale clinical datasets and state-of-the-art computational resources.
Opportunities to publish in top-tier journals and present at leading conferences.
A collaborative and intellectually stimulating research team with strong clinical and computational expertise.
Additional Job Details (if applicable)
Remote Type
Work Location
EEO Statement:
Mass General Brigham Competency Framework
At Mass General Brigham our competency framework defines what effective leadership looks like by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance make hiring decisions identify development needs mobilize employees across our system and establish a strong talent pipeline.
Employment Type
Full-Time
