Job Description
Join Nexus Quantum Labs at the forefront of 2026's technological revolution as we pioneer the next generation of quantum computing solutions. We're seeking a visionary Quantum Computing Research Scientist to design groundbreaking algorithms and protocols that will redefine computational boundaries. You'll collaborate with Nobel Prize-winning physicists and AI innovators in our state-of-the-art facility, where your work will directly impact breakthroughs in cryptography, materials science, and artificial intelligence. Our culture values intellectual curiosity, rapid prototyping, and audacious thinking – perfect for those who want to solve problems deemed impossible just yesterday.
We offer competitive compensation, equity packages, and unparalleled resources including access to IBM's quantum cloud infrastructure and our proprietary 512-qubit testbed. This role requires deep theoretical knowledge paired with hands-on experimentation skills to transform quantum theory into practical applications.
Responsibilities
- Design and implement novel quantum algorithms for optimization, simulation, and machine learning applications
- Lead research on quantum error correction protocols and fault-tolerant computing architectures
- Develop hybrid quantum-classical workflows for real-world industrial applications
- Collaborate with hardware engineers to co-design next-generation quantum processors
- Publish peer-reviewed research and present findings at top-tier conferences
- Mentor junior researchers and contribute to quantum computing education initiatives
- Secure external research funding through NSF and DARPA grant applications
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 3+ years postdoctoral experience
- Expertise in quantum programming languages (Q#, Qiskit, Cirq) and circuit optimization
- Published research in Nature/Science or equivalent high-impact journals
- Proficiency in quantum error correction and fault-tolerance frameworks
- Demonstrated experience with quantum machine learning algorithms
- Strong background in linear algebra, probability theory, and computational complexity
- Ability to translate theoretical concepts into experimental prototypes