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Quantum Computing Researcher

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
1 Juli 2026
Deadline
1 Jul 2027

Job Description

Join Nexus Labs at the forefront of quantum innovation as we pioneer the computational frontier of 2026. We seek a visionary Quantum Computing Researcher to develop next-generation algorithms and solve previously unsolvable problems in cryptography, materials science, and artificial intelligence. Our state-of-the-art lab in Austin offers unparalleled resources to transform theoretical models into practical breakthroughs.

Collaborate with Nobel laureates and industry disruptors in an environment where curiosity meets cutting-edge technology. We provide competitive equity packages, flexible R&D budgets, and dedicated quantum hardware access for groundbreaking research.

Responsibilities

  • Design and implement novel quantum algorithms for optimization and simulation
  • Develop error mitigation protocols for fault-tolerant quantum systems
  • Lead cross-functional teams to translate quantum solutions into real-world applications
  • Publish breakthrough research in top-tier journals and industry conferences
  • Secure government and private funding for quantum computing initiatives
  • Advise product development teams on quantum technology integration
  • Mentor junior researchers in quantum computing principles

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (or equivalent experience)
  • 3+ years hands-on experience with quantum programming languages (Q#, Qiskit)
  • Published research in quantum algorithms or quantum error correction
  • Expertise in quantum hardware architectures (superconducting, ion trap, photonic)
  • Strong background in linear algebra and probability theory
  • Experience with high-performance computing environments
  • Proven ability to secure research grants and patents
  • Excellent communication skills for technical and non-technical audiences

Required Skills

Quantum Computing Qiskit Q# Cryptography Machine Learning Linear Algebra Research Fault-Tolerant Systems

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