Sort by
Refine Your Search
-
agreement. Each fellow will work with 1-2 faculty mentors on research projects that cover a broad range of environmental and agricultural economics topics and methods. Faculty mentors for this program will
-
phenotypes. The lab uses a variety of experimental (functional genomic, targeted genetic) and computational (bioinformatics) tools on human and mouse tissues and using in vitro methods on human cells
-
. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
-
cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
-
Postdoctoral Research Fellow position in statistics, genetics, and biomedical AI. The lab develops cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical
-
learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
-
with bioinformatic pipelines and approaches for working with methylation data, or a willingness/ability to learn these methods. The appointment is for one year with possibility of renewal based
-
skills in quantitative methods and data analysis software, writing skills 2. Research experience in related areas 3. Demonstrated ability to work independently, under supervision, and as part of a team
-
of diverse measurement methods for detecting airborne infectious disease threats. A critical aspect of your role will be ensuring robust, sensitive sensor performance with real-world samples, and effectively
-
-derived cells (iPSCs, myogenic progenitors), genetic mouse models, and phenotypic drug screening methods. In addition, we are examining severe muscle damage that results in a less efficient regenerative