Research Fellow

Research Fellow (Computer Engineering/Computer Science/Electronics Engineering) page is loaded Research Fellow (Computer Engineering/Computer Science/Electronics Engineering) Apply locations NTU Main Campus, Singapore time type Full time posted on Posted 5 Days Ago job requisition id R Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a leading college that is known for its excellent curriculum, outstanding and impactful research, and world-renowned faculty. Today, we are ranked #2 for AI and Computer Science by US News Best Global Universities; and #8 for Data Science and AI by QS World University Ranking. A hot bed of cutting-edge technology and groundbreaking research, the College aims to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. The College of Computing & Data Science (CCDS) invites applications for the position of Research Fellow . Key Responsibilities: The Research Fellow will work on a Vision-Language project to conduct the research on visual reasoning, vision and language, causal reasoning. The role of this position includes: Development of visual reasoning methods for vision and language, including visual question answering, visual dialog, visual commonsense reasoning, visual captioning, object detection, video analysis. Development of causal reasoning tools, including causal inference, counterfactual analysis, causal discovery. Development of deep learning methods on computer vision. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Minimum 3 years of related work experience. At least 3 topic-related publications in top-tier journals and conferences, including CVPR, ICCV, ECCV, NeurIPS, ICML, AAAI, TPAMI, IJCV, TIP, TMM. Deep understanding of the theory of Machine Learning, Deep Learning, Computer Vision and Causal Inference. Experience in at least one Deep Learning framework such as Tensorflow, Pytorch, MXNet and Programming Languages such as Python, Matlab, R and/or C/C++. Demonstrated project experience related to causal inference will be an advantage. Good written and oral communication skills. We regret that only shortlisted candidates will be notified. Hiring Institution: NTU Similar Jobs (5)Research Fellow (Computer Engineering/Computer Science/Electronics Engineering) locations NTU Main Campus, Singapore time type Full time posted on Posted 5 Days Ago Research Fellow (Computer Engineering/Computer Science/Electronics Engineering) locations NTU Main Campus, Singapore time type Full time posted on Posted 17 Days Ago Research Fellow (Computer Engineering/Computer Science/Electronics Engineering) locations NTU Main Campus, Singapore time type Full time posted on Posted 20 Days Ago NTU is also home to world-class autonomous institutes – the National Institute of Education, S Rajaratnam School of International Studies, Earth Observatory of Singapore, and Singapore Centre for Environmental Life Sciences Engineering – and various leading research centres such as the Nanyang Environment & Water Research Institute (NEWRI) and Energy Research Institute @ NTU ( ). Ranked amongst the world’s top universities by QS, NTU has also been named the world’s top young university for the past seven years. The University’s main campus is frequently listed among the Top 15 most beautiful university campuses in the world and has 57 Green Mark-certified (equivalent to LEED-certified) buildings, of which 95% are certified Green Mark Platinum. Apart from its main campus, NTU also has a campus in Novena, Singapore’s healthcare district. Under the NTU Smart Campus vision, the University harnesses the power of digital technology and tech-enabled solutions to support better learning and living experiences, the discovery of new knowledge, and the sustainability of resources. #J-18808-Ljbffr