We are seeking a talented Quantum Algorithms Researcher to join our applied research team. The successful candidate will develop and apply quantum and quantum-inspired algorithms to high-dimensional, real-world problems where computational efficiency and memory optimization are critical. This role sits at the intersection of cutting-edge quantum research and industrial application, requiring both deep theoretical understanding and an impact-oriented mindset.

Key Responsibilities

• Design, develop, and benchmark quantum and quantum-inspired algorithms for high-dimensional problems, with particular focus on partial differential equation (PDE) solvers, optimization, and quantum simulation.
• Apply tensor network methods (e.g., tensor trains) to complex and chaotic classical systems such as computational fluid dynamics, turbulence modeling, traffic forecasting, and option pricing.
• Research and implement error mitigation and error correction schemes for near-term quantum hardware.
• Benchmark quantum approaches against state-of-the-art classical algorithms to rigorously assess the practical potential of quantum technologies.
• Collaborate with industry partners on applied projects spanning portfolio optimization, energy simulation, noise characterization, and resource optimization.
• Communicate technical results effectively to diverse audiences, including academic peers, industrial stakeholders, and non-specialists.
• Publish research findings in peer-reviewed venues and present at international conferences and workshops.

Required Qualifications

• PhD (completed or near completion) in Physics, Quantum Information, Applied Mathematics, or a related field, with a specialization in quantum and/or quantum-inspired algorithms.
• Strong background in theoretical physics, quantum simulation, and open quantum systems.
• Demonstrated expertise in tensor network methods and their application to high-dimensional problems.
• Proven track record of peer-reviewed publications in quantum algorithms, tensor networks, or computational physics.
• Proficiency in Python and scientific computing tools; experience with high-performance computing environments (e.g., Slurm) and version control (GitHub).
• Experience translating research into applied, near-term solutions for industrial use cases.

Preferred Qualifications

• Experience collaborating with industry partners in sectors such as finance, energy, oil & gas, or hardware providers.
• Familiarity with computational fluid dynamics, turbulence, and reduced-order modeling.
• Background in quantum error mitigation and correction on real hardware.
• Experience presenting at international conferences and delivering invited talks.
• Additional technical skills in Mathematica, LaTeX, and JavaScript.