Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
equation models for Epstein–Barr Virus (EBV) and HIV-1 infection dynamics in human lymphoid tissue. New mathematical models will be informed by longitudinal experimental data, including multiplexed spatial
-
multidisciplinary translational neuro-oncology research program focused on immunotherapy, biomarker discovery, and spatial biology. In this role, you will help advance innovative research embedded within investigator
-
. Support maintenance of the Duke Forest’s GIS database, including GPS data collection, producing spatially explicit management records, and performing basic cartography and spatial analysis tasks. Assist in
-
Contribute to the analysis of single-cell RNA-seq, spatial transcriptomics, and multi-omic datasets Apply computational and machine learning approaches to build mechanistic biological models and rationally
-
-edge technologies, including genetically engineered mouse models, patient-derived models, single-cell and spatial genomics, organoid systems, and preclinical therapeutic studies. Learn more about our
-
for protein/enzyme design, drug discovery, foundation models of neural activity and virtual cells, drug-target interactions, biomarker discovery, or other AI/ML approaches for spatial, multi-omic, genomic, and
-
-epithelial interactions. The role employs a multi-modal research approach spanning genetically engineered mouse models, histopathology, mouse and human organoids, CRISPR-Cas9 gene editing, single-cell
-
Developing custom data models and algorithms for applied research Assisting with database design, development, and testing Supporting the setup and maintenance of data handling systems Ensuring data security
-
for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
-
will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology