Tenure-Track Assistant Professor Opening at Singapore University of Technology and Design

My department is looking for a new tenure track faculty member with expertise in high performance computing applied to many-body quantum systems! Here is the job posting. Interested potential candidates are welcome to contact me with any questions about working at SUTD or the application process.

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The Singapore University of Technology and Design (SUTD) is a young and growing university with a unique structure and mission in the vibrant nation of Singapore. SUTD features a focus on design from an engineering and technological perspective, an intimate student to faculty ratio, an innovative active-based learning pedagogy, an interdisciplinary pillar organization, a stellar faculty, and a beautiful new campus. SUTD was established in 2009, in collaboration with MIT and Zhejiang University, as the fourth publicly funded university in Singapore. SUTD is also considered by international experts as an emerging leader in engineering education:  http://news.mit.edu/2018/reimagining-and-rethinking-engineering-education-0327

 

The Science, Mathematics and Technology (SMT) cluster has an open position to hire a tenure-track Assistant Professor with a strong record of scholarly research in high performance computing applied to many-body quantum systems such as condensed matter, quantum chemistry, quantum simulation or others. We are particularly interested in candidates with a background in tensor networks and/or neural quantum states. Postdoctoral experience is desired. We seek candidates with an open mind towards multidisciplinary research and whose research area, methods and/or tools can impact multiple fields and society. We can consider more senior candidates too, e.g. Assoc. Prof. and Prof.

 

The candidate will join a young and growing department including other experts in many-body quantum systems working in areas such as quantum simulation and quantum computation, quantum error correction, quantum-inspired computing, quantum open systems, quantum transport, quantum thermodynamics, photonics etc. Furthermore, Singapore offers a stimulating and well-funded research environment with many experts in town.

 

Candidates must be committed to excellence in teaching at the undergraduate and graduate levels and to developing and maintaining an active research program. Candidates should be able to teach undergraduate courses in mathematics, physics or chemistry. We are particularly interested in individuals with a strong and genuine interest in promoting STEM education at all levels. The successful candidate can look forward to internationally competitive remuneration, attractive research startup packages and grant opportunities, and assistance for relocation to Singapore.

 

Additional information about the university and the SMT cluster and SUTD can be found at www.sutd.edu.sg and https://smt.sutd.edu.sg/.

 

Application Requirements

 

Applications will be accepted online at https://careers.sutd.edu.sg/ and the review of applications will close on 4 January 2026.

 

Candidates should submit their full application packages, which should include:
 
•    Complete resume with full publication list (Including Statement of interest / Cover letter)
•    Research statement/plans
•    Teaching statement/plans
•    3 Research papers
•    Contact information of 3 referees

arXiv highlights

Quantum-noise-limited optical neural networks operating at a few quanta per activation

Suitably-trained optical neural networks can still perform classification tasks accurately using low intensity light with a low signal to noise ratio. This suggests that specialized light-based analogue hardware for machine learning may offer a route towards reducing the enormous energy consumption of neural networks!

Dissipative mean-field theory of IBM utility experiment

Another approach towards reproducing the results of IBM's kicked Ising model quantum simulation experiment, this time using mean field theory. The Appendix gives a simple rule of thumb for estimating the quantum volume of specific devices based on their two-qubit gate and readout fidelities and compares some different hardware providers.

Maximally-Localized Exciton Wannier Functions for Solids

Wannier functions - localized states constructed as a superposition of Bloch waves from an energy band of interest - are an important tool of the condensed matter physicists' trade. This work presents a method for constructing maximally-localized Wannier functions for multi-particle states, focusing on applications to excitons (electron-hole pairs).

Tensorized orbitals for computational chemistry

This work presents a tensor network-based compression of the matrix elements that need to be computed and stored when performing quantum chemistry calculations, based on Tensor Cross Interpolation. This is yet another example of how tools from quantum many-body physics can be used to speed up time-consuming computational tasks - no working quantum computer needed!

Tensor network simulations challenge claims of quantum advantage...again!

Hot off the arXiv today: Efficient tensor network simulation of IBM's kicked Ising experiment

The authors report efficient classical simulations of the experiments by the IBM quantum computing team reported in Nature last week: Evidence for the utility of quantum computing before fault tolerance

What's going on here?

Tensor network methods are proving to be extremely powerful for computations related to quantum systems and large-scale neural networks. They work best for simulations of 1-dimensional or tree-like quantum systems (corresponding to the special case of matrix product states). Higher-dimensional systems or those with long range coupling containing looped paths, however, incur increasing overheads.

The Eagle quantum processor used in IBM's recent experiments is based on a two-dimensional network of qubits on a "heavy hexagon" grid. Thus, even though it is two-dimensional (harder for tensor network methods), its loops are longer than that of a more compact square lattice. The time required to traverse a single loop is comparable to the circuit depths probed in the experiment, meaning that by applying some clever factorization tricks the dynamics can be reproduced by efficiently-simulable tree-like tensor networks!

 

This is not the first time tensor networks have challenged claims of supremacy - they have also been used to simulate Google's original quantum supremacy experiments. What is particularly striking here is that the time between the publication of the quantum experiment and publication of the classical reproduction has dropped from years to weeks!

Here are some libraries for trying out tensor network simulations of quantum systems:

tensorcircuit: Python library developed by Tencent Quantum Lab - can handle shallow circuits involving hundreds of qubits.

ITensorNetworks: Julia library developed by the Flatiron Institute, which was used to reproduce the IBM experiments.

For theorists, getting familiar with these simulation tools that can also be applied to other important areas (such as large-scale machine learning or numerical simulations) seems to be a better use of time than getting to grips with the intricacies of ever-changing device-specific error models and quantum error mitigation schemes!