Research Assistant in Quantum Algorithms and Machine Learning

Research Assistant in Quantum Algorithms and Machine Learning Job Description We are seeking a promising Research Assistant to join an advanced research project at the intersection of quantum computing and machine learning, focused on developing scalable and coherent training methods for quantum models. This project tackles one of the central challenges in quantum machine learning: how to train quantum systems efficiently and reliably on scaling quantum hardware. The research will develop novel optimization frameworks based on quantum dynamics and apply them to advanced learning architectures. Main Responsibilities Develop and implement quantum learning models Design and evaluate novel quantum optimization and training algorithms Perform large-scale simulations using frameworks such as Qiskit and PennyLane Potentially execute experiments on state-of-the‑art quantum hardware Benchmark quantum models against classical and hybrid baselines Contribute to research publications at leading venues Develop reusable, high-quality research code and contribute to open-source tools Qualifications Master’s or PhD in Computer Science, Physics, Mathematics, or a closely related field Mathematical foundation, particularly in linear algebra, probability, and optimization Background or strong interest in quantum computing, quantum information, or quantum algorithms Knowledge of machine learning (e.g., deep learning, optimization, or learning theory) Programming skills in Python and experience with scientific computing (e.g., NumPy, SciPy, PyTorch) Experience with numerical simulations, algorithm design, or high-performance computing is advantageous Familiarity with quantum software frameworks (e.g., Qiskit, PennyLane, Cirq) is preferred Experience with research or advanced technical projects Analytical thinking, problem‑solving skills, and intellectual curiosity Ability to work both independently and collaboratively in a research environment Strong scientific writing and communication skills More Information Curriculum Vitae (CV) Academic transcripts Cover letter (1-2 pages) outlining research interests and relevant experience Sample of technical work (e.g., paper, report, or code repository) Contact information for 2 referees (Letters may be requested at a later stage) For more information, please reach out to View email address on click.appcast.io We encourage applications from candidates with diverse backgrounds. Applicants who do not meet all the listed criteria but are interested in the role are encouraged to apply. Req ID: 33632 Legal Branding guidelines Contact us Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. © National University of Singapore. All Rights Reserved. #J-18808-Ljbffr National University of Singapore