Job Description
Join the vanguard of computational evolution at Nexus Quantum Labs, where we're architecting the future of artificial intelligence through quantum innovation. As a Quantum AI Research Scientist, you'll pioneer breakthroughs at the intersection of quantum mechanics and machine learning, developing algorithms that redefine what's computationally possible.
We're seeking visionary researchers to transform theoretical quantum advantage into practical AI solutions. You'll collaborate with Nobel laureates and industry pioneers in our state-of-the-art facility, leveraging IBM Quantum and D-Wave systems to solve problems deemed impossible for classical computing. This role offers unparalleled opportunity to shape the technological landscape of 2026 and beyond.
Responsibilities
- Design and implement quantum neural networks for advanced pattern recognition and optimization
- Develop hybrid quantum-classical AI architectures for real-world applications
- Lead research initiatives in quantum machine learning algorithms and error correction
- Collaborate with cross-disciplinary teams to deploy quantum AI solutions in finance, healthcare, and logistics
- Author peer-reviewed publications and contribute to open-source quantum AI frameworks
- Mentor junior researchers and drive innovation through quarterly quantum hackathons
- Secure research grants and partnerships with leading quantum hardware providers
Qualifications
- PhD in Quantum Computing, Physics, Computer Science, or related field (or equivalent research experience)
- Expertise in quantum programming languages (Qiskit, Cirq, Q#) and quantum circuit design
- Proven track record of publishing in Nature/Science/IEEE journals on quantum or AI topics
- Mastery of Python, TensorFlow/PyTorch, and high-performance computing frameworks
- Deep understanding of quantum entanglement, superposition, and decoherence mitigation
- Experience with quantum annealing and variational quantum algorithms
- Strong background in linear algebra, probability theory, and computational complexity