Research Assistant in Quantum Algorithms and Machine Learning

This position is for a Research Assistant focusing on developing scalable and coherent training methods for quantum machine learning models. Key responsibilities include developing and implementing quantum learning models, designing novel quantum optimization algorithms, and performing simulations. A Master's or PhD in a related field with a strong mathematical foundation, background in quantum computing or machine learning, and Python programming skills are required.

• 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

• Strong scientific writing and communication skills
• 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