Sort by
Refine Your Search
-
the production, value, and safety of pecan, peach, nectarine, and plum crops. The postdoctoral fellow will help in implementing experiments to develop novel methods of controlling economically important pests
-
) participant, you will join a community of scientists and researchers in an effort to optimize collection methods and protocols for human biofluids. This project will be in support of the Air and Space
-
the guidance of a mentor, this opportunity will involve: developing and applying methods in computational biology and artificial intelligence to gather information about gene function in the legume family; using
-
businesses to develop new technologies that enhance the nutritional and functional qualities of plant-derived foods and to develop novel methods to detect contaminants and contents in foods that affect food
-
to integrate diverse perspectives and convergent methods in addressing complex social dimensions of wildland fire management. Mentor: The mentor for this opportunity is David Flores (david.flores2@usda.gov
-
transcriptomic methods, including but not limited to comparative genomics, population genomics, RNA-seq, genome assembly, variant calling, and other high-throughput sequencing approaches. Advanced proficiency in
-
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
-
of basic laboratory assays (i.e. ELISA, flow cytometry, western blots, cell culture or other molecular and cellular biology methods), immunological/virological assays (i.e. neutralization assays, plaque
-
, pharmacovigilance, pharmacoepidemiology methods development). In general you will have opportunities to learn: Understanding of pharmacovigilance workflows; GenAI based algorithm and agent development for causality
-
, or unsupervised learning methods. Proficiency in Python and familiarity with scientific computing libraries such as PyTorch, TensorFlow, Pandas, NumPy, and related ML frameworks. Experience with large datasets