Job Description
Join NexusLabs at the forefront of technological innovation as we pioneer the next era of computational power. We're seeking a visionary Quantum Computing Research Scientist to develop breakthrough algorithms and protocols that will redefine industries by 2026. In this role, you'll collaborate with Nobel laureates and industry disruptors in our state-of-the-art Austin R&D facility, where your work will directly shape the future of artificial intelligence, cryptography, and materials science.
This position offers unparalleled resources, including access to IBM Quantum and IonQ systems, and the opportunity to publish groundbreaking research in top-tier journals. Our team operates at the intersection of theoretical physics and practical application, creating solutions that solve today's most complex problems while preparing for tomorrow's technological landscape.
Responsibilities
- Design and implement quantum algorithms for optimization and machine learning applications
- Develop error correction protocols to achieve fault-tolerant quantum systems
- Collaborate with hardware teams to translate theoretical models into practical quantum circuits
- Lead research initiatives in quantum cryptography and secure communication protocols
- Publish peer-reviewed research and present findings at international conferences
- Mentor junior researchers and cross-functional teams on quantum computing principles
- Secure grant funding through NSF and DoD quantum research programs
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 3+ years industry experience
- Proficiency in quantum programming languages (Qiskit, Cirq, Q#) and simulation frameworks
- Published research in quantum computing or quantum information theory
- Deep understanding of quantum algorithms (Shor's, Grover's, VQE) and complexity theory
- Experience with superconducting or trapped-ion quantum hardware
- Strong background in linear algebra, probability, and statistical mechanics
- Track record of translating theoretical concepts into scalable implementations