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Postdoctoral Fellow-MSH-32910-033

Mount Sinai Health System
United States, United Statesfull_timeVerifiedPosted 23 Jul 2026
💰 $80,000/yr($74,692/yr$80,000/yr)

About the role

Postdoctoral Fellow
Department: Windreich Department of Artificial Intelligence and Human Health
Link to Lab: https://aisinai.org/


Details of Research Project
The Postdoctoral Fellow will join the AIMs Lab within the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai. The AIMs Lab develops innovative artificial intelligence methods that integrate multimodal biomedical data—including electronic health records, physiological waveforms, wearable device data, clinical text, medical imaging, genomics, and other multi-omic datasets—to improve precision medicine and patient care. Research spans predictive modeling, digital phenotyping, disease subtyping, clinical decision support, and AI-driven biomarker discovery, with applications across pediatric and adult populations.
The postdoctoral fellow will contribute to the development of next-generation AI models that leverage large-scale clinical and biological data to better predict disease risk, monitor patients in real time, and identify new therapeutic opportunities. Projects may involve foundation models for healthcare, multimodal machine learning, representation learning, explainable AI, and translational applications that bridge computational research with clinical implementation.

Technical Duties

  • Develop and implement AI/ML models for multimodal biomedical, physiological, and clinical data

  • Perform data preprocessing, analysis, and integration across multiple data modalities

  • Develop interpretable and explainable machine learning methods for biomedical applications

  • Contribute to computational pipelines, reproducible workflows, and internal software tools

  • Prepare manuscripts, figures, and visualizations for publications and presentations

  • Collaborate with faculty, clinicians, and researchers across Mount Sinai

  • Participate in lab meetings, seminars, workshops, and collaborative research projects

  • Participate in lab meetings, seminars, workshops, and collaborative projects

Educational and Other Requirements for the Position

  • PhD in Computer Science, Biomedical Informatics, Computational Biology, Bioinformatics, Statistics, Biomedical Data Science, Engineering, or a related quantitative field

  • Strong programming skills (e.g., Python, R)

  • Background in machine learning, deep learning, statistical modeling, or related areas

  • Experience working with large datasets and computational methods

  • Strong written and oral communication skills

Experience Required

  • Demonstrated experience in AI/ML, data science, or computational biology

  • Experience with electronic health records, physiological data, wearable sensor data, genomics, medical imaging, clinical text, or multimodal biomedical data (preferred but not required)

  • Prior publication record in relevant fields

Goals/Outcomes

  • Develop novel artificial intelligence methods for precision medicine and clinical prediction

  • Build multimodal AI models that integrate clinical, physiological, genomic, and wearable data

  • Discover clinically actionable biomarkers and improve disease stratification

  • Advance real-time patient monitoring and AI-driven clinical decision support

  • Prepare high-impact manuscripts and conference presentations

  • Advance scientific understanding of human health through data-driven methods

 

 

Compensation Statement

The Mount Sinai Health System (MSHS) provides salary ranges that comply with the New York City Law on Salary Transparency in Job Advertisements. The salary range for the role is $74,692.00 - $80,000.00 Annually. Actual salaries depend on a variety of factors, including experience, education, and operational need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefit

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Company

Mount Sinai Health System

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