Research Fellow I

SUTD is the world’s first Design AI university. With Design AI, artificial intelligence is treated as a partner and a member of the team – not just a tool. As a result of this unique SUTD treatment, AI and humans brainstorm, spar and prototype together, resulting in solutions that are elevated several-fold. This human-AI team concept has been made possible because of SUTD’s unique cohort-based interdisciplinary pedagogy. As a trailblazer in the field of design and technology education and research, SUTD has been pioneering innovative programmes and initiatives since its formation 16 years ago. As part its new mid-term growth strategy called SUTD Leap, it will work closely with industry to co-create solutions in both education and research – for a better world.

Job Title: Postdoctoral Research Fellow in Quantum-Enhanced Optimisation and High-Performance Quantum Simulation

Job Description:

We are seeking a highly motivated and skilled Postdoctoral Research Fellow to join a research project developing physics-informed and quantum-enhanced methods for large-scale combinatorial optimisation. The successful candidate will apply tensor-network methods, quantum many-body techniques, and high-performance computing to develop and simulate quantum mean-field annealing and quantum sampling-based optimisation algorithms for applications such as Max-Cut, scheduling, routing, and resource allocation.

Key Responsibilities:

1. Quantum Many-Body and Tensor-Network Methods

Develop quantum mean-field annealing and related physics-inspired algorithms by mapping combinatorial optimisation problems to Ising or QUBO Hamiltonians.

Apply tensor-network and quantum many-body methods to study ground states, optimisation landscapes, phase behaviour, and annealing dynamics.

Benchmark the proposed approaches on challenging optimisation instances and compare them with established classical solvers.

2. High-Performance Simulation and Implementation

Develop scalable simulations of quantum annealing processes and parameterised quantum circuits using GPU, FPGA, or other high-performance computing platforms.

Optimise numerical algorithms for large-scale tensor-network and quantum-circuit calculations.

Contribute to the development of research software and proof-of-concept implementation on available quantum hardware.

Required Qualifications:

- Ph.D. in Condensed Matter Physics, Computational Physics, Quantum Information, Computer Science, Electrical Engineering, or a related field.
- Demonstrated experience in one or more of the following:

• Tensor-network methods or quantum many-body simulation
• Numerical studies of quantum spin systems
• GPU, FPGA, or parallel computing
• Quantum algorithms, quantum annealing, or combinatorial optimisation

- Strong programming skills in Python, C++, Julia, CUDA, or similar languages.

- Experience in machine learning, reinforcement learning, QUBO formulations, or quantum-computing frameworks is advantageous but not essential.

- A strong publication record and the ability to conduct independent research while collaborating effectively within an interdisciplinary team.