Postdoctoral Fellow-MSH-32030-004
$72.5k - $80kInternal Medicine
Postdoctoral Research Fellow — Medical Imaging & Deep Learning
Department / Lab: Medical Intelligence Lab, Department of Diagnostic, Molecular, and Interventional Radiology Institution: BioMedical Engineering and Imaging Institute Location: New York City (in person) Appointment: Full-time, [1]-year term (renewable, subject to funding and performance) Reports to: Dr. Xueyan Mei, PhD Start date: [Date / As soon as possible]
About the Position
We are seeking a highly motivated Postdoctoral Research Fellow to join the Medical Intelligence lab and contribute to research at the intersection of medical imaging analysis and machine learning. The successful candidate will design, train, and validate deep learning models on clinical imaging data, publish in leading venues, and collaborate with clinicians, data scientists, and engineers. This role is well suited to someone who wants to translate methodological advances into tools with real clinical impact.
Project Description
This position centers on building large-scale foundation models for medical imaging and translating them into clinically meaningful applications. The work spans three connected threads:
- Large-scale vision-language model development. Design and train large multimodal vision-language models that jointly reason over medical images and associated text (reports, clinical notes, structured data), with an emphasis on scalable pretraining, efficient fine-tuning, and robust evaluation.
- Retinal imaging foundation model. Build and adapt a foundation model for retinal imaging (fundus photography and OCT) that can be pretrained on large image collections and transferred efficiently to a range of downstream tasks with limited labeled data.
- Downstream clinical applications. Apply and adapt these models to real-world clinical problems, including systemic and hematologic conditions such as multiple myeloma and sickle cell disease , where retinal and multimodal biomarkers may support early detection, risk stratification, and disease monitoring.
The successful candidate will help move the group's work from general-purpose model development toward validated, clinically relevant tools, working closely with clinical collaborators throughout.
ResponsibilitiesKey Responsibilities
- Develop, train, and evaluate deep learning models for medical image analysis (classification, segmentation, detection, and related tasks).
- Build reproducible experimental pipelines in PyTorch, including data preprocessing, model training, and rigorous validation.
- Curate, clean, and manage imaging datasets while adhering to data governance, privacy, and ethics requirements.
- Design and run experiments, analyze results, and iterate on model architectures and training strategies.
- Write and publish first-author papers in peer-reviewed journals and top-tier conferences.
- Contribute to grant proposals, progress reports, and presentations to internal and external stakeholders.
- Collaborate with clinical partners to define problems, interpret results, and ensure clinical relevance.
Required Qualifications
- PhD in Computer Science, Biomedical Engineering, Electrical Engineering, Applied Mathematics, Medical Physics, or a closely related field (completed, or defended before the start date).
- Demonstrated experience in medical imaging analysis (e.g., MRI, CT, X-ray, ultrasound, OCT, or fundus imaging).
- Strong proficiency in PyTorch and modern deep learning workflows.
- Solid programming skills in Python and familiarity with the scientific computing stack (NumPy, pandas, scikit-learn, etc.).
- Track record of peer-reviewed publications appropriate to career stage.
- Strong analytical, written, and verbal communication skills, and the ability to work both independently and collaboratively.
Preferred Qualifications
- Experience working with electronic health records (EHR) and integrating structured clinical data with imaging (multimodal modeling).
- Experience with retinal imaging (fundus photography, OCT) and related tasks such as diabetic retinopathy or glaucoma analysis.
- Familiarity with medical data standards and privacy frameworks (e.g., DICOM, HL7/FHIR, HIPAA/GDPR).
- Experience with segmentation frameworks, self-/semi-supervised learning, foundation models, or model interpretability.
- Experience with version control (Git), containerization (Docker), and HPC or cloud GPU environments
What We Offer
- A collaborative, interdisciplinary research environment with access to clinical data and domain experts.
- Access to GPU clusters / compute resources / imaging datasets.
- Support for conference travel, publication, and professional development.
- Dedicated mentorship and a clear commitment to your growth as an independent researcher. We will support you in developing your research agenda, publishing high-impact work, building collaborations, and preparing for the next step in your career, whether in academia or industry.
- Mount Sinai standard benefits package, relocation support.
How to Apply
Please submit the following to View email address on careers.mountsinai.org with the subject line "Postdoc — Medical Imaging & Deep Learning":
- Cover letter describing your research interests and fit for the role.
- Curriculum vitae, including a full publication list.
- Names and contact details of [2–3] references.
- (Optional) Links to representative code, projects, or a Google Scholar profile.
Strength through Unity and Inclusion
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 unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture that ensures fair access to opportunities, promotes inclusive practices, and supports the success of every individual.
At Mount Sinai, our leaders are committed to fostering a workplace where all employees feel valued, respected, and empowered to grow. We strive to create an environment where collaboration, fairness, and continuous learning drive positive change, improving the well-being of our staff, patients, and organization. Our leaders are expected to challenge outdated practices, promote a culture of respect, and work toward meaningful improvements that enhance patient care and workplace experiences. We are dedicated to building a supportive and welcoming environment where everyone has the opportunity to thrive and advance professionally. Explore this opportunity and be part of the next chapter in our history.
About the Mount Sinai Health System:
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, more than 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 — discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients’ medical and emotional needs at the center of all treatment. The Health System includes more than 9,000 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News & World Report's Best Hospitals, receiving high "Honor Roll" status, and are highly ranked: No. 1 in Geriatrics, top 5 in Cardiology/Heart Surgery, and top 20 in Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News & World Report’s “Best Children’s Hospitals” ranks Mount Sinai Kravis Children's Hospital among the country’s best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is ranked No. 11 nationwide in National Institutes of Health funding and in the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. Newsweek’s “The World’s Best Smart Hospitals” ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally.
Equal Opportunity Employer
The Mount Sinai Health System is an equal opportunity employer, complying with all applicable federal civil rights laws. We do not discriminate, exclude, or treat individuals differently based on race, color, national origin, age, religion, disability, sex, sexual orientation, gender, veteran status, or any other characteristic protected by law. We are deeply committed to fostering an environment where all faculty, staff, students, trainees, patients, visitors, and the communities we serve feel respected and supported. Our goal is to create a healthcare and learning institution that actively works to remove barriers, address challenges, and promote fairness in all aspects of our organization.
Compensation
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 $72500 - $80000 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 benefits.
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