Doctoral Student

Nanyang Technological University Singapore; Singapore; On-site; SGD 36,000 - 49,000; Full time

Job summary

NTU Singapore's School of Electrical and Electronic Engineering invites applications for PhD positions in AI efficiency. The lab focuses on co-designing algorithms and hardware to maximize performance within real latency and power budgets, spanning embodied AI, IC design, efficient algorithms, and biosignal models.

As a PhD student, you will work on frontier problems, publish in top ML and hardware venues, receive close mentorship, collaborate with international partners such as ETH Zürich,

Qualifications

• Bachelor's or Master's in EE, CS, or Computer Engineering or related field.
• Solid foundations in math and programming (e.g., PyTorch, C/C++, or Verilog/SystemVerilog).
• Genuine curiosity about how AI models and hardware fit together.
• Prior research experience or publications is a plus but not required.

Responsibilities

• Work on frontier problems in efficient AI and publish at top ML and hardware venues.
• Receive close, hands-on mentorship and grow into an independent researcher.
• Collaborate across the algorithm–hardware boundary with partners such as ETH Zürich, Stanford, and MIT.

Skills

Math foundations

Programming (Python/PyTorch)

C/C++

Verilog/SystemVerilog

Education

Bachelor's or Master's degree in EE/CS/Computer Engineering

Tools

Verilog/SystemVerilog

PyTorch

RISC-V

Job description

PhD positions in AI efficiency at NTU Singapore!

I recently joined the School of Electrical and Electronic Engineering at Nanyang Technological University as a Nanyang Assistant Professor, and I'm building a lab on AI efficiency across the full stack, from algorithms down to silicon. Our view is simple: the biggest efficiency gains come from co-designing the algorithm and the hardware that runs it.

• Embodied AI systems: software-hardware co-design for robotics, making VLAs and world models run within real latency and power budgets
• IC design & EDA: open-source RISC-V processors and AI accelerators, PPA optimization, and 3D integration
• Efficient algorithms: compression and acceleration of LLMs, VLMs, and VLAs (quantization, pruning, distillation, KV-cache compression)
• Biosignal foundation models: efficient EEG/EMG models deployed on wearables

As a PhD student, you will

• work on problems at the frontier of efficient AI and publish at top ML and hardware venues
• get close, hands-on mentorship and grow into an independent researcher
• collaborate across the algorithm–hardware boundary with our partners, including ETH Zürich, Stanford, and MIT
• a Bachelor's or Master's degree in EE, CS, Computer Engineering, or a related field
• solid foundations in math and programming (e.g., PyTorch, C/C++, or Verilog/SystemVerilog)
• genuine curiosity about how AI models and the hardware beneath them fit together
• prior research experience or publications are a plus, but not required

We're recruiting for the Spring 2027 and Fall 2027 intakes. Admission follows NTU's standard cycle, so please check the application deadline for the intake you're targeting. Stipend and benefits follow NTU's standard policies.

Why NTU

NTU is a research-intensive university with one of the most beautiful campuses in the world, and a vibrant community of researchers from 100+ nationalities. Applicants from all over the world are warmly welcome.

More about the lab: https://ofsoundof.github.io/

Please share with students who might be a great fit!

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