Research Associate (Computer Engineering/Electrical Engineering/Computer Science)

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.

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 Research Associate role is responsible for designing and implementing advanced hardware architectures and system-level solutions for next-generation computing, with a focus on memory-centric acceleration.

Key Responsibilities:

• Design and develop Processing-in-Memory (PIM) architectures, particularly leveraging emerging memory technologies such as ReRAM

• Implement and optimise hardware acceleration solutions for scientific computing algorithms (e.g., iterative solvers such as AMG, CG) and AI workloads

• Conduct hardware-software co-design, including architecture design, dataflow optimisation, and system integration

• Develop novel mapping strategies and dataflow scheduling techniques to improve performance, energy efficiency, and hardware reliability

Job Requirements:

• Master’s degree in Computer Engineering, Electrical Engineering, Computer Science, or related discipline

• Strong background in computer architecture, hardware acceleration, or high-performance computing

• Experience in Processing-in-Memory (PIM), ReRAM, or emerging memory technologies is highly preferred

• Proficiency in algorithm-hardware co-design, particularly for sparse or irregular computations

• Familiarity with scientific computing algorithms (e.g., AMG, Conjugate Gradient, SpMV) or AI acceleration

• Experience in hardware modelling, simulation, or architecture design tools

• Strong understanding of performance optimisation, energy efficiency, and system-level trade-offs

• Ability to work independently and collaboratively in a multidisciplinary research environment

• Strong analytical, problem-solving, and communication skills

• Track record of publications, patents, or research projects will be an advantage

We regret that only shortlisted candidates will be notified.