Postdoctoral Fellow-MSH-32910-034

Mount Sinai; United States; On-site; USD 75,000 - 80,000; Full time

Job summary

Mount Sinai invites applications for a Postdoctoral Data Science Research Fellowship in Artificial Intelligence based in New York City. The role focuses on AI applications to novel devices, cardiac waveform processing, EHR data, and clinical care.

The successful candidate will join a multidisciplinary team of data scientists, engineers and clinicians to drive breakthroughs that are scalable and clinically meaningful. Strong publication history and English fluency are desirable.

Qualifications

• PhD or MD/PhD in a quantitative science-related field (biomedical informatics, clinical informatics, machine learning, biostatistics, etc.).
• Significant ML experience, ideally with published work and/or code.
• Expertise with Python and statistical software.
• Experience with CNNs, Transformers, ECG embeddings, GNNs and DL frameworks is preferred.

Responsibilities

• Join a dynamic team of data scientists, engineers and clinicians.
• Apply deep learning to AI-driven cardiovascular devices and clinical data.
• Contribute to scalable, clinically meaningful research breakthroughs.

Skills

ML techniques

Python proficiency

English fluency

Statistics background

Communication

Education

PhD or MD/PhD in quantitative field

Tools

TensorFlow

PyTorch

Keras

d3rlpy

Job description

The Windreich Department of Artificial Intelligence and Human Health and the Mount Sinai Fuster Heart Hospital at the Icahn School of Medicine at Mount Sinai are inviting applications for a Postdoctoral Data Science Research Fellowship position in Artificial Intelligence (AI) based in New York City.

This role focuses on applications to novel devices, cardiac waveform processing, electronic health records (EHR) data, and clinical care.

Key topics of interest include, but are not limited to:

• Deep learning applications to novel waveform acquisition devices
• Multimodal data (structured, unstructured, images, waveforms)
• Deep learning based clustering and predictive modeling for disease diagnosis and risk stratification
• Graph neural networks
• Personalized therapeutic recommendations
• Development of AI based clinical decision support systems

We have a specific interest in applications for cardiovascular disease and applications to novel device technologies.

The postdoctoral fellow will join a dynamic team of data scientists, engineers, and clinicians. They will participate in unique opportunities to apply deep learning for important scientific breakthroughs that are scalable, clinically meaningful, and operationally pragmatic.

The ideal candidates will have the following background:

1. PhD, MD/PhD in a quantitative science-related field (e.g., biomedical informatics, clinical informatics, machine learning, biostatistics, etc.).
2. Significant experience in machine learning techniques is preferred, ideally with published work and/or code available. Expertise with CNNs, Transformers, ECG embeddings, GNNs, and Deep Learning frameworks is preferred (e.g. d3rlpy, Tensorflow, PyTorch, Keras).
3. Expertise with programming and statistical software experience with advanced Python proficiency.
4. A strong statistics, data science, and/or engineering background is advantageous.
5. The ideal candidate will have a track record of academic publication and likes to be creative.
6. Strong communication and presentation skills with fluency in spoken and written English.

Information on the Postdoctoral Training Program at Mount Sinai: https://lnkd.in/giyhRNwr.

To learn more about the Icahn School of Medicine at Mount Sinai: http://icahn.mssm.edu.

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 benefits.

SPOC-UAW Local 4100 at Icahn School of Medicine (Post Docs), J18 - AI and Human Health - ISM, Icahn School of Medicine