Postdoctoral Research Scientist on Artificial Intelligence and Machine Learning in Biomedicine

The Laboratory of AI & Biomedicine Science (LABS) at the Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID) at the Radiology Department at Columbia University has multiple open postdoctoral positions. The interests of LABS are to develop and apply statistical, machine learning, and artificial intelligence (AI/ML) methodologies to "big data" in multi-omics and medical data for aging and diseases, such as Alzheimer’s disease. We emphasize utilizing advanced AI/ML techniques and multi-omics data, including MRI, genetics, transcriptomics, and proteomics, to solve clinically relevant problems for precision medicine. All relevant directions can be pursued based on discussions, interests, and expertise. We are seeking highly motivated individuals with excellent academic track records and related expertise: Ph.D. degree in any quantitative science, preferably in Computer Science, Electrical and Computer Engineering, Biomedical Engineering, Applied Mathematics, and Neuroscience. Candidate should have a working knowledge of programming, including Linux, Python, and R. Candidates having background knowledge in neuroimaging, machine learning, and/or genomics/genetics are encouraged. Excellent communication and writing skills are required. Underrepresented individuals/students are encouraged to apply. Successful applicants will join a vibrant research environment and work closely with computational scientists and clinical investigators. Collaborators include faculty in our Radiology department, the Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), the New York Genome Center (NYGC), the Department of Biomedical Engineering (BME), and across the Columbia University Irving Medical Center (CUIMC). Columbia University is an Equal Opportunity Employer / Disability / Veteran Pay Transparency Disclosure The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University’s good faith and reasonable estimate of the range of possible compensation at the time of posting.