Sort by
Refine Your Search
-
received within the past five years. Strongly preferred skills: Experience with mathematical modeling of biological systems Experience with population modeling, disease modeling, or similar ecological
-
of microphysiological systems or organ on a chip model for viral agents. Research project emphasis is placed on determining virus growth and stimulation of an appropriate immune response that mimics what is observed in
-
group selection management to promote structural complexity, making them a useful test of different modeling approaches in the Forest Vegetation Simulator (FVS). Currently FVS users, which largely consist
-
. Along the way, you will engage in activities and research in several areas. Learning activities will focus on: The development and characterization of animal models and/or microphysiological systems
-
. Qualifications The ideal candidate should have a strong background in the mathematical and computational aspects of modeling subsurface and surface flows. Knowledge in machine learning, data assimilation, and
-
research-operational partnerships and learning about systems involving forest fuels and fire emissions modeling. They will gain experience with modeling, coding, and database management in support of a
-
related orthopaedic trauma. In particular, contemporary cell / molecular biology in vitro approaches as well as clinically relevant small and large animal models of orthopaedic trauma are utilized
-
fellow for a research project titled, “Structural modification and update of the U.S. national harvested wood products carbon model (WOODCARB II)”. USDA Forest Service has been using the WOODCARB II model
-
approaches will include species distribution models and integration of spatially explicit environmental variables including climate, hurricane disturbance, and land-use, among other global and Caribbean region
-
with a team focused on developing new systems and novel techniques and tools, such as improving smoke modeling systems through a variety of means. This may include multi-model approaches, statistical and