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AI-Assisted Identification of Undiagnosed and Misdiagnosed hEDS/HSD Cases
Position summary
The Canada Excellence Research Chair (CERC) in Genomic Medicine at McGill University is seeking a highly motivated Postdoctoral Researcher to join the hEDS*Omics Study, a multi-centre research initiative focused on improving recognition of hypermobile Ehlers-Danlos syndrome (hEDS) and hypermobility spectrum disorder (HSD). This position will focus on developing and applying AI-based approaches to identify patients who may be undiagnosed, misdiagnosed, or delayed in diagnosis. The successful candidate will use clinical notes, structured phenotypes, medical terminology, ICD codes, and available omics and health-data resources to detect patients with clinical patterns suggestive of hEDS/HSD. The position is inspired by recent advances in AI-based platforms for rare disease diagnosis, including systems that process free-text clinical descriptions, Human Phenotype Ontology (HPO) terms, and genetic testing results to produce ranked diagnostic hypotheses with traceable reasoning and evidence support.
Main responsibilities
The Postdoctoral Researcher will work closely with the CERC research team and collaborators to:
- Develop and evaluate AI-based pipelines to identify potentially undiagnosed or misdiagnosed hEDS and HSD cases.
- Use clinical text, physician notes, ICD codes, structured symptoms, HPO terms, and available omics data to detect diagnostic patterns.
- Design approaches for extracting and normalizing clinical phenotypes from unstructured medical records from the hEDS*Omics Study and international cohort studies.
- Compare AI-generated candidate cases against known diagnoses, clinical criteria, chart review, or expert assessment.
- Support validation studies to assess sensitivity, specificity, positive predictive value, and clinical usefulness of the AI-based case-finding approach.
- Prepare reproducible scripts, analysis documentation, technical reports, figures, manuscripts, and grant materials.
- Collaborate with clinicians, geneticists, data scientists, bioinformaticians, and international consortium partners.
Required qualifications
The ideal candidate should have:
- A PhD in computational biology, bioinformatics, computer science, data science, machine learning or a related field.
- Strong experience working with clinical, biomedical, genetic, electronic health record, or health administrative data.
- Experience with large language models, natural language processing, medical text mining, or clinical AI.
- Strong programming skills in Python and/or R.
- Familiarity with medical terminology, ICD codes, phenotype extraction, or HPO.
- Ability to work with sensitive health data in a secure and privacy-conscious research environment.
- Strong scientific writing skills and ability to contribute to manuscripts, grants, and technical documentation.
- Excellent organizational skills, attention to detail, and ability to work independently and collaboratively.
- Good communication skills in English or French.
Preferred qualifications
Experience with any of the following would be considered an asset:
- LLM-based clinical decision support, rare disease diagnosis, or diagnostic reasoning systems.
- Retrieval-augmented generation, agentic AI workflows, prompt engineering, or model evaluation.
- Clinical phenotype extraction from free text.
- Electronic health records, OMOP, FHIR, ICD-code-based phenotyping, or healthcare data models.
- Chart review, diagnostic validation, or clinician-in-the-loop AI evaluation.
- Python tools for NLP/LLMs, such as Hugging Face, LangChain, LlamaIndex, spaCy, or similar frameworks.
This position will be funded by the Canada Excellence Research Chair (CERC) in Genomic Medicine and Mitacs.
Annual salary
Postdoc Researcher unionized (Category C): $48000 CAD (+benefits)
Note that this position is conditional upon approval of Mitacs funding. If the funding is not approved, the appointment may not proceed.
Before applying, please note that to work at McGill University, you must be both authorized to work in Canada and willing to work in the province of Quebec at the campus where the position is based / located.
McGill University is an English-language university where most teaching and research activities are conducted in the English language, thereby requiring English communication both verbally and in writing.
Annual Salary:
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McGill University hires on the basis of merit and is strongly committed to equity and diversity within its community. We welcome applications from racialized persons/visible minorities, women, Indigenous persons, persons with disabilities, ethnic minorities, and persons of minority sexual orientations and gender identities, as well as from all qualified candidates with the skills and knowledge to productively engage with diverse communities. McGill implements an employment equity program and encourages members of designated groups to self-identify. Persons with disabilities who anticipate needing accommodations for any part of the application process may contact, in confidence, [email protected].
Skills Required
- PhD in computational biology, bioinformatics, computer science, data science, machine learning or related field.
- Strong experience working with clinical, biomedical, genetic, electronic health record, or health administrative data.
- Experience with large language models, natural language processing, medical text mining, or clinical AI.
- Strong programming skills in Python and/or R.
- Familiarity with medical terminology, ICD codes, phenotype extraction, or Human Phenotype Ontology (HPO).
- Ability to work with sensitive health data in a secure and privacy-conscious research environment.
- Strong scientific writing skills and ability to contribute to manuscripts, grants, and technical documentation.
- Excellent organizational skills, attention to detail, and ability to work independently and collaboratively.
- Good communication skills in English or French.
- LLM-based clinical decision support, rare disease diagnosis, or diagnostic reasoning systems.
- Retrieval-augmented generation, agentic AI workflows, prompt engineering, or model evaluation.
- Clinical phenotype extraction from free text.
- Experience with electronic health records, OMOP, FHIR, ICD-code-based phenotyping, or healthcare data models.
- Chart review, diagnostic validation, or clinician-in-the-loop AI evaluation.
- Experience with Python tools for NLP/LLMs such as Hugging Face, LangChain, LlamaIndex, or spaCy.
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