176 parallel-processing-bioinformatics research jobs at Nanyang Technological University
Sort by
Refine Your Search
-
Listed
-
Program
-
Field
-
and approaches to improve the software engineering process in Continental, especially requirement engineering and testing Conducting the research in combining AI techniques with formal methods
-
simulate orbital scenarios for sensor calibration and data fusion. Model complex orbital dynamics for accurate sensor calibration. Develop AI models for onboard and ground-based satellite data processing
-
of deliverables. Lead the design and execution of surface modification processes for fiber materials, including chemical grafting, functional coating, and plasma or other physical treatments. Develop and optimize
-
Assistant to conduct research in advance diamond technology. The role will focus on Conduct an in-depth review of diamond material properties for thermal applications and quantum sensing. Develop new process
-
, lamination, and testing. He/she will contribute to the development of new application driven materials and production processes, located mostly at Nanyang Technological University. Key Responsibilities: Lead
-
/ machine learning algorithms to support research in the IDMxS Analytics Cluster. The RF will apply/ improve machine learning algorithms to process (e.g., classify, predict) data collected by IDMxS. Help
-
team to conduct the research on development of AI models and algorithms for image processing, computational imaging as well as computer vision applications. The roles of this position include: Research
-
team to conduct the research on development of AI models and algorithms for image processing, computational imaging as well as computer vision applications. The roles of this position include: Research
-
, 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
-
, 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