188 post-doc-computer-vision Fellowship positions at Nanyang Technological University
Sort by
Refine Your Search
-
Research Fellow / Associate Research Fellow / Senior Analyst / Research Analyst (Maritime Security Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang
-
Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in
-
Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in
-
Associate Research Fellow / Research Fellow (Military Transformations Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang Technological University
-
Research Analyst/Senior Analyst/Associate Research Fellow (China Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang Technological University, Singapore
-
on high-speed vision perception for autonomous driving. This project aims to advance the state of the art in visual perception algorithms and real-time systems for autonomous racing, pushing the boundaries
-
for publications. Job Requirements: PhD in organic chemistry At least 5 years of experience in synthetic organic chemistry and/or asymmetric catalysis as PhD/post-doc in advanced institutions. Solid analytical
-
bioengineering, aquaculture systems, and computer vision. Job Requirements: PhD or Senior Scientist in Bioengineering, Computer Vision, Environmental Technology, Biomedical Engineering, Aquatic Biology, or a
-
computational materials science techniques (DFT, MD, machine learning force fields) with data-driven approaches. Design and implement high-throughput experimental workflows for thermal conductivity and phonon
-
, 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