74 algorithm-development-"https:"-"Simons-Foundation" Postdoctoral positions at Duke University
Sort by
Refine Your Search
-
this position; no new applications will be accepted after 2025/12/18 11:59PM US Eastern Time. Position Description Postdoctoral Associate in Synthetic/Medicinal Chemistry Location: The Hong Group (https
-
brain evolution. We employ a multifaceted strategy to bridge developmental neurobiology, RNA biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu
-
. Example references for previous work include: https://www.nature.com/articles/s41586-023-06786-y https://www.nature.com/articles/s41467-022-33497-1 https://doi.org/10.1016/j.actamat.2024.119667 https
-
cultured cells and animal models of skin diseases. Work Performed • Development of new and implementation and modification of existing experimental procedures. • Data preparation for oral presentations
-
address questions on bacterial responses to antibiotics, the implications of horizontal gene transfer in community dynamics and evolution, predictive assembly of complex microbial communities, and
-
background and interests and contact information only for at least three references to AcademicJobsOnline.org https://academicjobsonline.org/ajo/jobs/31488. ; We will begin to review applications on February
-
welcome to apply. • Candidates should have a developing track record of research output • Programming proficiency in python is essential • Commitment to open research, as appropriate to the discipline
-
activities, including a Bass Connections project. The postdoctoral associate will co-develop a research project related to critical minerals. Potential project topics are diverse, drawing on expertise of Hub
-
questions that lie at the interface of organic chemistry and chemical biology. The candidate will develop new chemical methods and small-molecule based probes to advance understanding of important biological
-
new NIH-funded Center for Excellence in Multiscale Immune Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks