Sort by
Refine Your Search
-
flux measurements using biogeochemical modeling. They will be responsible for managing projects related to field instrumentation and ecosystem flux modeling. 20% designing and implementing trace gas flux
-
neural populations and their application in animal models In the Department of Neurosurgery. There will be opportunities to lead a team of students, contribute to grant writing, engage in professional
-
and then model their space use and behavioral patterns. The post-doctoral researcher will also be responsible for coordinating a team to deploy and monitor behavioral playback cameras, developing a data
-
with innovative modeling methods and data analytics methods and spur cross-discipline development between the team in both water resources and computer science. Specifically, the research projects
-
or more of the following: ● Experience with urban watershed modeling or lake systems modeling ● Experience with limnological or aquatic field methods ● Experience with statistical methods for making
-
modeling, physiological signal analysis, and innovative neuromodulation strategies for neurological disorders such as epilepsy, chronic pain, and autonomic dysfunctions. Primary Responsibilities 35
-
Qualifications Essential Qualifications • PhD in mathematics, science or STEM education research or equivalent (e.g., PhD in biological field with dissertation on discipline-based education research) • Experience
-
and their application in animal models. There will be opportunities to lead a team of students, contribute to grant writing, engage in professional development, and disseminate results at conferences
-
skills in R programming - Working knowledge of Python - Experience with basic analyses to characterize gut microbiomes, including diversity analysis, differential abundance analysis, modeling microbial and
-
, computer vision in the Division of Health Data Science (HDS) at the DOS. The position is an annually renewable professional academic appointment. Duties/Responsibilities: ● Risk predictive model for clinical