Postdoctoral Research Fellow in Clinical NLP and LLMs

How to Apply:

Applications will be reviewed on a rolling basis until the position is filled. For application submission and inquiries, please contact: mzabihi@mgh.harvard.edu
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
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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).
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Build retrieval-augmented generation (RAG) pipelines (hybrid retrieval, citation enforcement) with safety guardrails for generating evidence-grounded outputs from ICU/ALS corpora.
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Mitigate hallucinations and rigorously evaluate robustness, calibration, and fairness across ICU subpopulations (age, sex, comorbidities) and ALS cohorts (site, disease stage, language).
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Deliver reproducible pipelines with versioned data, containers, unit tests, and transparent evaluation metrics.
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Apply explainable AI (XAI) (concept attribution, counterfactuals, clinician-readable rationales) to enhance model interpretability in ICU monitoring and ALS progression modeling.
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Collaborate with intensivists, neurologists, and data scientists to co-develop models aligned with real-world ICU workflows and ALS clinical research.
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Contribute to manuscripts, grant proposals, and dissemination of findings at leading conferences and journals.
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Ensure compliance with privacy regulations and participate in secure handling of sensitive ICU and ALS data.
Qualifications
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Ph.D. in Computer Science, Biomedical Engineering, Computational Neuroscience, Applied Mathematics, or related field.
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Strong expertise in machine learning and deep learning applied to clinical/biomedical data.
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Proven experience with transformers, LLMs, and modern NLP frameworks.
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Proficiency in Python, PyTorch, and related ML toolchains; experience with EHR/ICU note preprocessing and feature engineering.
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Track record of publications in AI/ML for healthcare or neuroscience.
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Strong problem-solving skills, independence, and collaborative mindset.
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Excellent communication skills for both technical and clinical audiences.
Preferred Skills
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Experience scaling experiments on cloud platforms (AWS, GCP, Azure) or HPC clusters.
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Knowledge of self-supervised learning, domain adaptation, and federated learning for cross-site generalization.
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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).
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Familiarity with neurophysiological data (EEG, telemetry) or neuroimaging is highly desirable.
What We Offer:
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A dynamic, interdisciplinary research environment at the forefront of AI in neuroscience and critical care.
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Access to large-scale clinical datasets and state-of-the-art computational resources.
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Opportunities to publish in top-tier journals and present at leading conferences.
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A collaborative and intellectually stimulating research team with strong clinical and computational expertise.