Research fellow clinical

Site: The Brigham and Women's Hospital, Inc.

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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: [email protected]
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: [email protected]

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

Onsite

Work Location

101 Merrimac Street

EEO Statement:

The Brigham and Women's Hospital, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. To ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Veteran’s Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990, applicants who require accommodation in the job application process may contact Human Resources at (857)-282-7642.

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