173 parallel-and-distributed-computing Fellowship research jobs at Nanyang Technological University
Sort by
Refine Your Search
-
secure and reliable research on microchips used in quantum key distribution (QKD) systems. The researcher will focus on investigating and identifying potential hardware-level security vulnerabilities and
-
necessary lead relevant meetings. To undertake any other duties relevant to the programme of research. Job Requirements: PhD degree in Computer Engineering, Computer Science, Electronics Engineering or
-
reinforcement learning using degraded human feedbacks. Develop distributed localization approaches for multi-agent systems. Investigate the suitability of various sensors for robot navigation in human-sense
-
, 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
-
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
-
, 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
-
applies AI to tackle challenges in aquaculture and drug delivery, working at the interface of materials science, biology, and computational modeling. Key Responsibilities: Lead and execute AI-driven
-
to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and
-
maritime transport, marine technology, computer science, or a related field; Excellent programming skills, such as Python, Matlab, C++, or other computer languages; A record of publications in reputable peer
-
, 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