251 phd-rehabilitation-engineering-computer-science Fellowship research jobs at Nanyang Technological University
Sort by
Refine Your Search
-
of research. Job Requirements: Preferably PhD in Computer Science or equivalent. Independent, highly analytical, proactive and a team player Excellent teamwork and verbal, written communication skills In-depth
-
Requirement: PhD degree in Electrical and Electronic Engineering and/or Electronic and Communication Engineering from top universities Expertise in Carbon Nanotubes material Experienced with Carbon Nanotubes
-
community. Requirement: PhD in computer science/ engineering or related fields. Proficiency in Python is a must. Experience with other programming languages such as Java, C/C++, or Go, and frameworks like
-
National Centre for Scientific Research (CNRS); Nanyang Technological University (NTU) and Thales. We are seeking to hire a candidate to support the project on passive cooling technology. Key
-
writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning and optimization & controls Having basic knowledge in carbon
-
updates to principal investigator and funding agency Report writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning
-
in Electrical and Electronic Engineering, Material Science, Physics or related disciplines. Experience in operating CVD systems for growth of 2D and other nanomaterials. Experience in nanomaterial
-
junior students/researchers Job Requirements: PhD degree from a reputable university in chemical engineering, environmental engineering, mechanical engineering, etc. Excellent journal paper publication
-
and contribute to evolving methodologies. Present findings to academic and industry stakeholders. Job Requirements: PhD in Biomedical Engineering, Biochemistry, Biology, Chemical Engineering
-
. Job Requirements: Preferably PhD in Computer Science or related field. Background and familiarity with the implementation and deployment of machine learning pipelines in embedded systems (e.g., robotic