399 algorithm-development-"The-University-of-Edinburgh" Fellowship positions in Singapore
Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
-
decarbonisation and digitalisation, methodology development and applications Publish findings in top peer-reviewed journals and conference proceedings Collaborate with other researchers on project discussions and
-
, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets. Collaborate with multidisciplinary teams including biologists, engineers, and clinicians
-
, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
-
on AI-driven end-to-end autonomous driving algorithms. Key Responsibilities: The research fellow will be leading the development of AI-driven end-to-end autonomous driving algorithms. The work will
-
) Design robust obstacle avoidance algorithms for mobile robots in dynamically changing environments, focusing on formal safety constraints and real-time performance in unpredictable conditions. b) Develop
-
for the position of Research Fellow for the project Anticipatory Robotics. Project Introduction: The project titled Anticipatory Robotics aims to improve Human-Robot Interaction by developing a “Robot Body Language
-
validate advanced 5G features such as network slicing, MEC and xApp/rApp. Contribute to the development of innovative solutions and algorithms to enhance 5G network capabilities. Work closely with
-
Centre for Advanced Robotics Technology Innovation (CARTIN) is looking for a candidate to join them as a Research Fellow. Key Responsibilities: Develop novel algorithms for multi-agent inverse
-
(terrestrial and NTN). The goal of this research is to design and develop algorithms and techniques that adapt to the environment, minimizing signaling overhead associated with channel estimation and enhancing