Job Description
Join Nexus Quantum Labs at the forefront of 2026's technological revolution as we pioneer the convergence of quantum computing and artificial intelligence. We seek a visionary Quantum AI Research Scientist to architect the next generation of intelligent systems that will redefine human-machine collaboration. This role offers unparalleled opportunities to shape the future of computational intelligence while working alongside Nobel laureates and Turing Award winners in our state-of-the-art research facility.
Our 2026 Visionary Program provides dedicated resources for groundbreaking research, flexible experimentation protocols, and direct access to quantum hardware. You'll develop novel algorithms for quantum machine learning, optimize quantum neural networks, and solve previously intractable problems in cryptography, materials science, and complex systems modeling. This position includes comprehensive benefits, equity participation, and funding for continuous learning and conference participation.
Responsibilities
- Design and implement quantum machine learning algorithms leveraging quantum supremacy principles
- Develop hybrid quantum-classical neural architectures for enhanced computational efficiency
- Lead research initiatives in quantum-entangled AI models for predictive analytics
- Collaborate with hardware engineers to optimize quantum algorithms on next-gen processors
- Publish findings in top-tier journals (Nature, Science, etc.) and present at IEEE/ACM conferences
- Secure research grants and patents for breakthrough quantum AI methodologies
- Mentor postdoctoral researchers and lead cross-functional innovation sprints
Qualifications
- Ph.D. in Quantum Computing, Machine Learning, or Computational Physics (or equivalent experience)
- Expertise in quantum algorithms (Shor's, Grover's, QAOA) and quantum circuit design
- Proficiency in Python, TensorFlow Quantum, Cirq, and high-performance computing frameworks
- Published research in quantum machine learning or quantum information science
- Experience with superconducting qubit platforms or ion trap systems
- Strong background in deep learning architectures (CNNs, RNNs, transformers)
- Ability to translate complex theoretical concepts into practical implementations