54 mathematics-graph-theory Fellowship research jobs at Nanyang Technological University
Sort by
Refine Your Search
-
) who is highly skilled in and deeply passionate about computational electromagnetism and mathematical physics/engineering. The SRF should have strong background in computational methods for solving
-
. Investigate and build robust data and AI agent pipelines for continuous learning and knowledge acquisition, including annotation strategies and knowledge graph development for aquaculture stress events. Design
-
Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research
-
Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research
-
Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research
-
/Research Fellow is also expected to support teaching activities as required by the school. We are looking for someone with experience in power engineering and a strong mathematical background. Key
-
Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research
-
tutorials. Job Requirements: Master degree (Research Associate) and PhD (Research Fellow) in Electrical Engineering, Computer Science, Mechanical Engineering, or other related fields. Solid Mathematical and
-
Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research
-
Responsibilities: Conduct programming and software development for graph data management. Design and implement machine learning models for optimizing graph data management. Conduct experiments and evaluations