123 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" Fellowship positions at Zintellect
Sort by
Refine Your Search
-
and weaknesses for end-users. Help develop new or improve existing soil moisture estimates using NISAR and other datasets utilizing artificial intelligence (AI) and machine learning. The outcome from
-
& Amputation Center of Excellence (EACE) is a unique organization within the Department of War (DoW) consisting of teams of researchers embedded at the point of care within multiple Military Treatment Facilities
-
pathogens such as Japanese encephalitis and Rift Valley fever. Learning Objectives: The fellow will learn epidemiological techniques related to modeling parasitic and vector-borne diseases. Opportunities
-
diseases such as Japanese encephalitis, Rift Valley fever, and related diseases. Learning Objectives: The fellow will have opportunities to learn field-based techniques related to survey and manage arthropod
-
are offered an opportunity for an independent research project using lab data to gain experience conducting ecological data analysis, manuscript writing, and publishing in peer-reviewed journals. Learning
-
to unravel key indicators of biological relevance during seed quality testing procedures and contribute to a healthy national and international seed trade economy. Learning Objectives: Under guidance of a
-
documenting progress on data processing. Opportunities may also be available to participate in field data collection at various locations in the Pacific Northwest. Learning Objectives: As an educational
-
plants. The participant will learn and use multiple molecular biology, synthetic biology and plant biotechnology related tools and techniques including plasmid vector design and assembly, plant genetic
-
regions will be evaluated for features such as signatures of selection or diversifying or purifying selection, around genes and regions of agricultural importance. Learning Objectives: The participant will
-
phenotyping using both drone-based and ground based sensing platforms. Learn artificial intelligence and machine learning techniques to analyze image and geospatial data from diverse sources for crop monitoring