Opportunities – CAMCA About CAMCA Our Team Research Publications News and Events Opportunities Third Deep Recon Workshop Federated Learning for Medicine (FL4M) Menu About CAMCA Our Team Research Publications News and Events Opportunities Third Deep Recon Workshop Federated Learning for Medicine (FL4M) Opportunities Frequently Asked Questions Are you looking for students? Yes. As a multidisciplinary lab, we are open to students from various backgrounds and experience levels. What do I need to prepare for applying? There are no fixed requirements, yet applicants with publications in good international peer-reviewed journals are preferred and self-funded (e.g. via China Scholarship Council) applicants are preferred. Ph.D students who graduated in the U.S. are welcome to join as postdoctoral research fellows at MGH and Harvard Medical School. What projects are you currently working on? What project could I work on? Please refer to our research sections. How can I join your group? If you are interested, please contact Prof. Quanzheng Li at li.quanzheng@mgh.harvard.edu and attach your CV/Resume for more details. Current opportunities Machine Learning/Image Reconstruction Postdoctoral Fellows, Instructors and Assistant Professors The MGH/BWH Center for Clinical Data Science & Center for Advanced Medical Computing and Analysis Job Title: Postdoctoral Fellows / Instructor / Assistant Professor, MGH/BWH Center for Clinical Data Science & & Center for Advanced Medical Computing and Analysis, Harvard Medical School Location: Massachusetts General Hospital Responsibilities: Work collaboratively with a team of physicians, clinical researchers, mathematician, computer scientists, and engineers to solve important medical problems in a world-class research facility. MGH is the top-ranked research hospital in the United States, and the MGH Department of Radiology has an exemplary history of supporting imaging research. We have advanced computing facilities, access to a wealth of high-quality medical imaging and pathologic data, and we understand key problems in medicine. The MGH/BWH Center for Clinical Data Science (CCDS) is a joint effort from Massachusetts General Hospital, Brigham and Women’s Hospital and Harvard Medical School. The center is a cross-functional, cross-institutional group of clinicians, researchers, data scientists, product development and translational experts. The positions are jointly appointed by MGH Center for Advanced Medical Computing and Analysis (CAMCA) and CCDS. Our vision is to build a smart healthcare delivery system through the creation of an ever-learning and ever-changing set of machine learning-based tools and services. Our goal is to develop and deploy applications to empower clinicians and enhance outcomes. The research fellow / instructor / assistant Professor will work closely with the team to develop machine learning based method to solve clinical problems. The candidate will be expected to pro-actively develop his/her research interests in the area. Candidates who seek a long-term academic career will have tremendous opportunities to grow. Salary and Benefits: The position is full-time with benefits. The salary is commensurate with experience. To Apply: Applicants should send a cover letter describing research experience, interests and goals, a CV with a full list of publications, and link to GitHub profile (if available) as well as three references (including one from a current supervisor) to Pro. Quanzheng Li by email (li.quanzheng@mgh.harvard.edu). CVs and subsequent interviews will be reviewed by a multi-disciplinary team of clinicians, computer scientists and engineers. The Massachusetts General Hospital is an Equal Opportunity/Affirmative Action Employer. Requirements: The ideal candidate will have a PhD in Applied Mathematics, Computational Statistics, Computer Science, Bioengineering, Biomedical Engineering, Electrical Engineering or a related discipline, Experience and background of computer science and data science, computer vision, machine learning, artificial intelligence, and medical image reconstruction and analysis are required, Image reconstruction and analysis skills as well as proficiency in C++, MATLAB, Python, Java, R and Linux are highly desired, Experience with radiology image reconstruction analysis is preferred by not required, Demonstrated record of high-quality publications is required, Strong communication skills in written and verbal English are required, Qualified candidates should be highly self-motivated and possess the ability to work independently as well as in a multidisciplinary collaborative environment. Contact: Quanzheng Li, Ph.D (li.quanzheng@mgh.harvard.edu) Machine Learning/Image Reconstruction, Cardiac Focus Postdoctoral Fellows, Instructors and Assistant Professors The MGH/BWH Center for Clinical Data Science & Center for Advanced Medical Computing and Analysis Job Title: Postdoctoral Fellows / Instructor / Assistant Professor, MGH/BWH Center for Clinical Data Science & & Center for Advanced Medical Computing and Analysis, Harvard Medical School Location: Massachusetts General Hospital Responsibilities: Work collaboratively with a team of physicians, clinical researchers, mathematician, computer scientists, and engineers to solve important medical problems in a world-class research facility. MGH is the top-ranked research hospital in the United States, and the MGH Department of Radiology has an exemplary history of supporting imaging research. We have advanced computing facilities, access to a wealth of high-quality medical imaging and pathologic data, and we understand key problems in medicine. The MGH/BWH Center for Clinical Data Science (CCDS) is a joint effort from Massachusetts General Hospital, Brigham and Women’s Hospital and Harvard Medical School. The center is a cross-functional, cross-institutional group of clinicians, researchers, data scientists, product development and translational experts. The positions are jointly appointed by MGH Center for Advanced Medical Computing and Analysis (CAMCA) and CCDS. Our vision is to build a smart healthcare delivery system through the creation of an ever-learning and ever-changing set of machine learning-based tools and services. Our goal is to develop and deploy applications to empower clinicians and enhance outcomes. The research fellow / instructor / assistant Professor will work closely with the team to develop machine learning based method to solve clinical problems with a focus on cardiac specialties. The candidate will be expected to pro-actively develop his/her research interests in the area. Candidates who seek a long-term academic career will have tremendous opportunities to grow. Salary and Benefits: The position is full-time with benefits. The salary is commensurate with experience. To Apply: Applicants should send a cover letter describing research experience, interests and goals, a CV with a full list of publications, and link to GitHub profile (if available) as well as three references (including one from a current supervisor) to Pro. Quanzheng Li by email (li.quanzheng@mgh.harvard.edu). CVs and subsequent interviews will be reviewed by a multi-disciplinary team of clinicians, computer scientists and engineers. The Massachusetts General Hospital is an Equal Opportunity/Affirmative Action Employer. Requirements: The ideal candidate will have a PhD in Applied Mathematics, Computational Statistics, Computer Science, Bioengineering, Biomedical Engineering, Electrical Engineering or a related discipline Experience and background of computer science and data science, computer vision, machine learning, artificial intelligence, and medical image reconstruction and analysis are required, Image reconstruction and analysis skills as well as proficiency in C++, MATLAB, Python, Java, R and Linux are highly desired, Experience with radiology image reconstruction analysis is preferred by not required, Demonstrated record of high-quality publications is required, Strong communication skills in written and verbal English are required, Qualified candidates should be highly self-motivated and possess the ability to work independently as well as in a multidisciplinary collaborative environment. 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