117 machine-learning-"https:"-"https:"-"https:"-"https:"-"Ulster-University" Fellowship positions at Zintellect
Sort by
Refine Your Search
-
areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
-
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
-
of learning nonlinear, high-dimensional relationships between molecular attributes and biological responses. This project proposes to develop an AI/ML-integrated translational framework that associates
-
development. You will gain experience researching advanced in silico prediction algorithms, analyzing machine learning approaches for toxicity pattern recognition, and participating in developing standardized
-
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
-
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
-
assays from method development to experimental or test sample analysis including the process of method validation. Learning Objectives: Computational: Through this opportunity you will; Establish
-
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