-
include in vitro neural differentiation, gene expression manipulation, metabolic assays, and mouse breeding and behavior. Knowledge in basic computer skills, record keeping and experience with data
-
on human- and animal-related factors associated with companion animal relinquishment. The post-doctoral associate in this position will be expected to survey pet owners in communities within the Washington
-
for construction operations. The successful candidate will contribute to cutting-edge research in mixed reality (MR)-based simulation platforms, machine learning-based process optimization, and human-machine
-
Virginia Tech as an assistant professor in January 2026. Virginia Tech (Virginia Polytechnic Institute and State University) is an internationally recognized research university, particularly known for its
-
datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding of malignant
-
of appointment will be two years. The position is on an interdisciplinary team of mathematicians, psychologists, and engineers who are exploring the dynamics of stress contagion in human crowds. The postdoc will
-
of major human diseases including cancer and to develop effective new approaches to diagnostics and therapeutics. The FRBI has launched an ambitious collaboration with Children’s National Hospital to build
-
optogenetics, light-controlled gene expression systems, and photoreceptors. Familiarity with metabolic modeling (e.g., COBRA methods), genome-scale modeling, or flux balance analysis. Experience with Raman
-
and new collaborations within and outside Virginia Tech, and assist with the training of undergraduate and graduate students, as needed. Preferred Qualifications Experience in cardiac electrophysiology
-
microenvironment. The project will employ genetically engineered mouse and human glioma models, along with advanced imaging techniques, single-cell and spatial transcriptomics, and molecular biology approaches