📢 Open positions

I have have multiple openings in

• GenAI for computational biology: towards quantum computing

• Internship Positions

• Classical GenAI for biology - 2025

  • Postdoc
  • Research Assistant

• Quantum algorithms for Dynamical Systems - 2025

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Generative AI for biology

We invite excellent candidates in classical machine learning to join a collaborative research initiative between the MathEXLab of NUS (Mechanical Engineering) and the Centre for Quantum Technologies (CQT). The project benefits from access to both leading experts in the field and advanced quantum computing infrastructure.

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.

We currently have openings for:

• 1 Postdoctoral position

Application process

Deadline: The positions will remain open until filled, with interviews starting the 10th of December.

Applicants should submit to "".join([*("alessandro"[:3],chr(~-65)+".".join(["nus","ude"[::-1],"sg"]))]) the following materials:

1. A CV, including a list of publications.
2. A statement of research interests of ~2 pages.
3. Contacts of referees who can provide recommendation letters: two if you are a recent PhD graduate, or three if you already have postdoctoral experience.

   

Postdoctoral fellowship

Duration: 1 year
Location: Singapore
Affiliations: Mechanical Engineering in NUS and Centre for Quantum Technologies
Starting date: September 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.

Additional considerations:

• Familiarity with graph machine learning.
• Familiarity with representation theory.
• Familiarity with drug discovery.

Internship positions

We evaluate internship applications on a rolling basis. If dedicated funding is not available at the moment, we are open to exploring co-supervision arrangements with professors at partner institutions — even remotely. These arrangements allow the intern to engage in meaningful research under shared mentorship and may open alternative funding pathways or joint supervision models.

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