Description
We are inviting applications for a Postdoctoral Research Fellowship position at the Icahn School of Medicine at Mount Sinai in data science and machine learning with a specific interest in applications to electronic health records (EHR) and other modalities of patient data (e.g., imaging, genomics, electrophysiological waveform data). This work can have a particular focus on COVID-19 related research. More information: www.glicksberglab.com
Apply: To apply, please submit
- CV
- brief cover letter detailing proposed topics of study
- contact information of referrals
to benjamin.glicksberg@mssm.edu
We offer competitive compensation, a vibrant/supportive work environment, and a series of perks as outlined below.
Background:
This position will be in the lab of Dr. Benjamin Glicksberg (glicksberglab.com) housed in the Hasso Plattner Institute of Digital Health a Mount Sinai (HPIMS) in the Icahn School of Medicine at Mount Sinai in Manhattan, New York, NY, USA. HPIMS has a vested interested in using advanced data science methodologies on large-scale health data to push forward precision medicine.
Key topics of interest include but are not limited to: predictive modeling for disease outcomes and personalized therapeutic recommendations, working with common data model EHR standards (e.g., OMOP), multi-modal (e.g., genomics, imaging, electrophysiological, and clinical data) deep learning for clinical decision support, unsupervised learning to discover biologically-relevant disease subtypes, transforming real-world data to real-world evidence for supporting regulatory decisions, cost and comparative effectiveness for medicine in practice, and methods development to facilitate a learning health system, among others. We have an interest in many disease domains including nephrology, cardiology, radiology, psychiatry, and others.
Data and Resources
The candidate will have the opportunity to work with unparalleled data and computational resources. Data resources include:
(a) Access to >8 million patient EHR in the Mount Sinai Data Warehouse
(b) The BioMe Biobank Program with >30,000 patients with whole exome sequencing data linked to longitudinal clinical data;
(c) High-performance computing cluster as well as internal servers;
(d) Electrophysiological waveform data (ECG/EKG);
and (e) Various types of imaging data (e.g., MRI, x-ray).
The postdoctoral fellow will join a dynamic team of data scientists, computer scientists, geneticists, and clinicians and participate in unique opportunities to apply machine learning for important scientific breakthroughs and to directly impact patients’ lives in a clinical setting.The candidate will have the opportunity to develop their own research projects and to lead or participate in local as well as international collaborations.
Perks:
Information on the Postdoctoral Training Program at Mount Sinai. To learn more about the Icahn School of Medicine at Mount Sinai. Incoming post-doctoral fellows are eligible for affordable Mount Sinai Housing within walking distance of the medical school and of a wide range of amenities as well as visa sponsorship on a case-by-case basis.
About Our Organization: The Icahn School of Medicine at Mount Sinai is internationally recognized as a leader in groundbreaking clinical and basic science research and is known for its innovative approach to medical education. With a faculty of more than 3,400 in 38 clinical and basic science departments and centers, Mount Sinai ranks among the top 20 medical schools in receipt of National Institutes of Health grants. In its 2015 “America’s Best Graduate Schools” issue, U.S. News & World Report ranks the Icahn School of Medicine 14th out of 130 medical schools nationwide. Mount Sinai Medical Center is an equal opportunity/affirmative action employer. We recognize the power and importance of a diverse employee population and strongly encourage applicants with various experiences and backgrounds. Mount Sinai Medical Center–An EEO/AA-D/V Employer.
Keywords: Machine Learning, Data science, Electronic Health Records, Electronic Medical Records, Genomics, Genetics, DNA, Imaging, Artificial Intelligence, Multi-omics, Common Data Models, Causal Inference, Network Biology, Time Series Analysis, Predictive modeling, Deep Learning, Generative Modeling, Clustering, R, Python, Precision Medicine, Personalized Medicine.
Requirements
The ideal candidates will have the following background:
Qualifications:
- PhD, MD, or MD/PhD in a quantitative science-related field (e.g., biomedical informatics, clinical informatics, machine learning, biostatistics, genetics, etc.)
- Significant experience in machine learning techniques is required, ideally with published work and/or code available. Expertise with deep learning frameworks is preferred (e.g.,, Tensorflow, PyTorch, Keras).
- Expertise with programming and statistical software experience in Python and/or R.
- Excellent publication track record including conference papers and preprints (e.g., arxiv).
- Strong communication and presentation skills with fluency in spoken and written English.
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