Sort by
Refine Your Search
-
. Preferred Skills: Background in quantitative research, evidenced by peer-reviewed publications, academic presentations, or preprint manuscripts Knowledge of Bayesian statistics, mathematical optimization
-
medicines. Research Project: The purpose of this proposed project is to better understand the impact of proteinuria on elimination and PK of biological products to optimize the dose in patients with
-
, texture profile, and other key techno-functional properties. Learning Objectives: Under the guidance of a mentor, the participant will learn to: develop and optimize procedures for extracting plant proteins
-
available for enthusiastic candidates interested in furthering their interests/knowledge in the control of agricultural insect pests. The overall research goal of the group is to develop new pest management
-
and performing data collection and analysis pertaining to pre-clinical research for the development and optimization of drug products, advanced therapies for the treatment of hemorrhagic shock and
-
practices affect latex yield and quality; Demonstrate knowledge of latex extraction, purification, and concentration processes used in pilot-scale operations; Evaluate and optimize processing methods
-
application of foundation models for fungal DNA and protein sequences. With ARS and external AI knowledge-holders, you will adapt long-context DNA language models to fungal genomes. These DNA-language models
-
on understanding fundamental plant growth and developmental processes. You will advance your physiological and phenological knowledge on grapevine stress responses and enable identifying grapevines tolerant
-
data collected longitudinally across the post-infection study time course to optimize machine learning methods that predict disease outcomes and identify host factors and interactions that most heavily
-
stage of the biomonitoring project associated with the appointment. Analytical method development and optimization – You may gain experience designing and carrying out mentor-guided experiments