Postdoctoral Fellow-MSH-32910-033

About the Role

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.

Responsibilities

• 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

Requirements

• 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
• 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

About the Employer

The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai’s unrivaled legacy of achievement, education, and innovation as we work together to transform healthcare.

Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 48,000 employees working across eight hospitals, over 400 outpatient practices, more than 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time.

Pay

The salary range for the role is $72,500 - $80,000 annually. Actual salaries depend on a variety of factors, including experience, education, and operational need. The salary range does not include bonuses, incentive pay, differential pay, or other forms of compensation or benefits.