Sort by
Refine Your Search
-
contribute to overall lab operations. The applicant will be a collaborative, impact-focused problem solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber
-
opportunity to contribute to leading-edge research at the intersection of applied machine learning and clinical dental practice. As a member of our team, you will help translate contemporary data science
-
or analysis tools · Proficiency in, or a strong interest in learning computational data analysis. Additional Qualifications Special Instructions Required documents: · CV · Research summary of PhD work · Cover
-
-determination. In-depth consultation with community leaders, health experts, and other local knowledge carriers—and circulation of lessons learned through routine academic publication and community dissemination
-
to prioritize work and coordinate research protocols with lab members Excellent attention to detail and organization skills Interest in learning and strengthening existing skillsets Special Instructions We highly
-
engineered constructs. Learn more about the innovative work led by Dr. Chris Chen here: https://bdc.bu.edu/bdc-team/. What you’ll do: Independently conduct research on liver cell proliferation, expansion, and
-
quantitative methods at the interface of statistical learning, experimental design, and optimization to address challenges created by the operationalization of AI within partner organizations. The Postdoctoral
-
, and AI/machine learning would be helpful for the role. Experience with participant recruitment and retention as well as clinical human subject studies is a plus. Special Instructions Application
-
simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and human data, and a new emphasis on the importance of design and innovation
-
postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health, including experimental design and reinforcement