Postdoctoral Fellow-MSH-32910-011

We are inviting applications for a Postdoctoral Research Fellowship position in data science and artificial intelligence with a specific interest in applications to longitudinal electronic health records (EHR) and molecular data. This position will be jointly held between the Charles Bronfman Institute for Personalized Medicine, and the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai in Manhattan, New York, NY, USA.  These entities have a vested interest in using advanced artificial intelligence and data science methodologies 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, 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, aging, and others.

Dr. Beckmann will supervise the successful candidate. The candidate will have the opportunity to work with unparalleled data and computational resources. Data resources include (a) Access to >8 million patient records in the Mount Sinai Data Warehouse; (b) The BioMe Biobank Program with >60,000 patients with whole exome sequencing data linked to longitudinal clinical data; (c) The Mount Sinai Million Health Discoveries Program which will generate genetic data in over a million participants d) EKG waveform data for over 8 million patients and (e) various types of imaging data (e.g., MRI, x-ray). He or she will have the opportunity to develop their own research projects and to lead or participate in local as well as international collaborations. As advisors, we actively encourage the professional development of our lab members and facilitate presentation of exciting findings in pioneering conferences.  Some interesting projects that are currently ongoing include the development of approaches to infer disease timeline (e.g., the natural history of disease) from cross-sequential data and the extension of these approaches to imaging, EHR and multi-omics modalities.
 

The postdoctoral fellow will join a dynamic team of data scientists, geneticists, and clinicians and participate in unique opportunities to apply deep learning for important scientific breakthroughs and to directly impact patients’ lives in a clinical setting. 

PrioThe ideal candidates will have the following background: 

1. PhD, MD, or MD/PhD in a quantitative science-related field (e.g., biomedical informatics, clinical informatics, machine learning, biostatistics, genetics, etc.) 

2. Significant experience in machine learning techniques is preferred, ideally with published work and/or code available. Expertise with deep learning frameworks is preferred (e.g. Tensorflow, PyTorch, Keras). 

3. Expertise with programming and statistical software experience in R and/or Python.  

4. Formal statistics or epidemiological training is a strong advantage. 

5. Excellent publication track record. 

6. Strong communication and presentation skills with fluency in spoken and written English. 

To apply, please submit a CV and a brief cover letter to noam.beckmann@mssm.edu. 

Information on the Postdoctoral Training Program at Mount Sinai:http://icahn.mssm.edu/education/postdoctoral-training. To learn more about the Icahn School of Medicine at Mount Sinai: http://icahn.mssm.edu.Incoming postdoctoral 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. 

Keywords: Genomics, Genetics, DNA, Imaging, Artificial Intelligence, Multi-omics, Electronic Health Records, Electronic Medical Records, Common Data Models, Causal Inference, Network Biology, Time Series Analysis, Predictive modeling, Deep Learning, Machine Learning, Data Science, Generative Modeling, Clustering, R, Python, Precision Medicine, Personalized Medicine.

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