Postdoctoral Fellow-MSH-32910-015

Mount Sinai; United States; On-site; USD 81,000 - 99,000; Full time

Job summary

Mount Sinai's Icahn School of Medicine seeks a Postdoctoral Fellow to join its Artificial Intelligence & Human Health initiative, focusing on predictive modeling for cardiovascular disease (CVD) and mild cognitive impairment (MCI) using EHR and biobank data.

This position will leverage advanced NLP and deep learning to extract and harmonize phenotypic variables from unstructured and structured data for the DORIAN GRAY project.

Qualifications

• PhD in Biomedical Informatics, Computer Science, Computational Biology, or related field.
• Experience in healthcare AI, biomedical NLP, and multi-modal data integration.
• Proficiency in Python and machine learning frameworks.
• Familiarity with EHR systems, medical coding, and large-scale clinical datasets.
• Strong publication record in relevant research areas.

Responsibilities

• Apply large language models for cognitive score extraction from clinical notes.
• Build and validate predictive models using EHR and genomic data.
• Conduct phenotype creation and feature engineering for model optimization.
• Collaborate with multidisciplinary teams across clinical, informatics, and data science domains.
• Publish research findings in high-impact journals and present at scientific conferences.

Skills

Python

Machine learning frameworks

Biomedical AI

NLP

Education

PhD in Biomedical Informatics/Computer Science/Computational Biology

Job description

Position: Postdoctoral Fellow – AI in Healthcare
Salary: $90,000/year
Location: Icahn School of Medicine at Mount Sinai, New York, NY
Lab Website: https://icahn.mssm.edu/about/artificial-intelligence

Overview:
The Icahn School of Medicine at Mount Sinai seeks a Postdoctoral Fellow to join its Artificial Intelligence & Human Health initiative, focusing on predictive modeling for cardiovascular disease (CVD) and mild cognitive impairment (MCI) using EHR and biobank data. This position will leverage advanced NLP and deep learning to extract and harmonize phenotypic variables from unstructured and structured data for the DORIAN GRAY project.

• Apply large language models for cognitive score extraction from clinical notes.
• Build and validate predictive models using EHR and genomic data.
• Conduct phenotype creation and feature engineering for model optimization.
• Collaborate with multidisciplinary teams across clinical, informatics, and data science domains.
• Publish research findings in high-impact journals and present at scientific conferences.

Qualifications:

• PhD in Biomedical Informatics, Computer Science, Computational Biology, or related field.
• Experience in healthcare AI, biomedical NLP, and multi-modal data integration.
• Proficiency in Python and machine learning frameworks.
• Familiarity with EHR systems, medical coding, and large-scale clinical datasets.
• Strong publication record in relevant research areas.