103 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL" Fellowship positions at Zintellect
Sort by
Refine Your Search
-
attenuated centrin deleted Leishmania major parasites (LmCen-/-) using transcriptomic and metabolomic approaches. Learning Objectives: The opportunity to contribute on this project will allow you to acquire
-
of the opportunity involve various outdoor conditions requiring moderate exertion and traversing the landscape of the MEF. Additionally, the fellow will experientially learn about and participate in the Forest Service
-
expected to learn both independently and collaboratively within a multidisciplinary research team, contribute to experimental design and data analysis, publish findings in peer-reviewed journals, and
-
of MPS-based potency assays in assessing antibody products. Learning Objectives: If selected, you will learn how to: Handle pathogens safely, adhering to all the laboratory and personnel safety rules and
-
to exploit in their projects. Characterization of the alfalfa collection can aid in effective management of this important resource while improving and promoting its use by stakeholders. Learning Objectives
-
conditions. Anticipated outcomes of the project include criteria for selection of fungal genotypes that will be developed into new biocontrol products for mitigation of crop aflatoxin contamination. Learning
-
of biologic products. Learning Objectives: The fellowship will provide you structured training in applied regulatory science, linking process understanding of novel manufacturing technologies to evolving
-
motivated Postdoctoral Research Fellow for a learning opportunity using cutting-edge genomic and transcriptomic methodologies to drive innovative biological control strategies against high-impact invasive
-
, focusing on gene editing and molecular breeding techniques. While flowering in sugarcane is an undesirable trait for Florida farmers, it plays a crucial role in crossing and breeding. Through this learning
-
students and collaborate on aquatic ecology field projects in southeast Alaska wilderness watersheds. Learning Objectives: Learn about bioenergetic food web models to quantify food web energy fluxes between