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Quantum Computing Research Scientist

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
24 Mei 2026
Deadline
24 Mei 2027

Job Description

Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. Our Austin-based innovation hub is seeking visionary Quantum Computing Research Scientists to develop next-gen algorithms and hardware prototypes that will redefine computational boundaries. You'll collaborate with Nobel laureates and industry pioneers in a cutting-edge environment where theoretical physics meets practical application.

We offer competitive equity packages, flexible hybrid work arrangements, and unparalleled professional development opportunities. Our state-of-the-art facility includes on-site quantum annealers, advanced cryogenic labs, and dedicated AI research clusters.

Responsibilities

  • Design and implement quantum algorithms for optimization and simulation problems
  • Develop error correction protocols for scalable quantum systems
  • Lead cross-functional teams in prototyping quantum hardware components
  • Publish peer-reviewed research in top-tier journals and conferences
  • Secure federal and private research grants for quantum initiatives
  • Mentor junior researchers in quantum mechanics and computational theory
  • Collaborate with industry partners to commercialize quantum breakthroughs

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 3+ years of hands-on quantum algorithm development experience
  • Proficiency in quantum programming frameworks (Qiskit, Cirq, etc.)
  • Publication record in quantum computing or quantum information theory
  • Deep understanding of quantum error correction and fault tolerance
  • Experience with cryogenic systems and quantum hardware interfaces
  • Strong background in linear algebra, probability, and statistical mechanics

Required Skills

Quantum Computing Algorithm Development Error Correction Qiskit Cirq Cryogenic Systems Machine Learning Linear Algebra Fault Tolerance

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