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We invite applications for classical and quantum machine learning, as part of a collaborative research initiative[1] between Duke-NUS and the Centre for Quantum Technologies (CQT). The project benefits from access to both leading experts in the field and advanced quantum computing infrastructure, including recent partnerships[2] with leading industry players.
The successful candidate will work within a multidisciplinary team that combines classical and quantum algorithm design, software implementation, and applications in drug discovery and molecular modelling. The research will contribute to the development of both quantum and/or classical algorithms, supported by robust, production-grade implementations — with selected molecular candidates proceeding to experimental synthesis and validation in wet-lab settings.
More information: https://luongo.pro/openings/
Duration: 1+1 years. Location: Singapore. Starting date: (tentative) June 2026
We are looking for a researcher with demonstrated excellence in machine learning, showcasing potential to collaborate with quantum scientists. The ideal candidate has:
- A strong publication record that reflects both theoretical depth and conceptual clarity, including the ability to develop and communicate mathematical proofs, and to engage effectively in whiteboard-level reasoning and problem-solving.
- Solid experience with PyTorch (and ideally Lightning), together with the ability to design, implement, and train neural network models to solve concrete problems, producing code that is clean, reliable, and reproducible.
- Interest in tackling open problems in computational biology and drug discovery.
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[1] https://www.duke-nus.edu.sg/daisi/research/ai-quantum
[2] https://www.cqt.sg/highlight/2024-07-singapore-quantinuum-quantum-computing/
[3] https://www.quantinuum.com/press-releases/singapore-inks-mou-with-quantinuum-enabling-access-to-their-advanced-quantum-computer
Thank you!
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