Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
to quantify pathogen load in samples and metagenomics and bioinformatics to understand genetic diversity of the pathogen. Potential areas of research include genomic epidemiology, ecological surveillance, and
-
problems. Methodologies used by the fellow range from those requiring considerable modification to routine, and include plant genetics, molecular biology techniques, computational biology/bioinformatics
-
assessment of mutations) Demonstrated skill and practical experience in molecular biology techniques (e.g., nucleic acid purification, gene amplification and cloning, bioinformatic analysis of genomic data
-
breeding and molecular biology. Learning Objectives: As a result of this experience, the participant will: Learn methods to conduct transformation of sugarcane, Learn genomics, bioinformatic methods
-
, bioinformatic tools or analytical pipelines that quantify the diversity of RNA viruses infecting swine that may be deployed in online databases or interactive websites. Learning Objectives: During this project
-
signaling. Learning Objectives: The participant will gain skills in bioinformatics, genetics, data analysis, statistics, and artificial intelligence-based methods for protein modelling. The participant will
-
, and genomic approaches. The selected fellow will join our team under the guidance of the Research Leader/Supervisory Plant Pathologist, engaging in all aspects of breeding, genomics, and bioinformatics
-
. market access. The approach will include metagenomics and bioinformatics to understand genetic diversity of the pathogen. Learning Objectives: During this project, the participant will be involved in
-
and/or extracts, bioinformatic analysis to identify putative functions in bacteria and yeasts, and texture measurements on vegetable tissue. The candidate will participate in data collection and
-
Learning about protein design and engineering Exploring cell-based and cell-free screening Applying high-throughput screening Utilizing bioinformatics, machine learning, and other computational approaches