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Quantum Computing Researcher

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

Job Description

Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking visionary researchers to develop breakthrough algorithms that will redefine computational boundaries. Our state-of-the-art Austin facility offers unparalleled resources to accelerate your research in quantum cryptography, error correction, and machine learning integration.

This role offers competitive equity packages, flexible work arrangements, and direct collaboration with Nobel Prize-winning physicists. Shape the future of technology while advancing your career in one of America's most innovative tech ecosystems.

Responsibilities

  • Design and implement quantum algorithms for optimization problems
  • Develop error mitigation protocols for quantum hardware systems
  • Collaborate with hardware teams to improve qubit stability and coherence times
  • Publish research in peer-reviewed quantum computing journals
  • Mentor junior researchers in quantum programming frameworks
  • Secure funding through NSF and DoD grant applications
  • Present findings at international quantum computing conferences

Qualifications

  • PhD in Physics, Computer Science, or related field (or equivalent experience)
  • 3+ years hands-on experience with quantum computing frameworks (Qiskit, Cirq, or Q#)
  • Published research in quantum information theory or quantum algorithms
  • Proficiency in Python and high-performance computing environments
  • Demonstrated understanding of quantum error correction techniques
  • Experience with superconducting or trapped-ion quantum systems
  • Strong analytical and problem-solving abilities
  • Excellent written and verbal communication skills

Required Skills

Quantum Computing Quantum Algorithms Error Correction Qiskit Cirq Python High-Performance Computing Superconducting Qubits Trapped-Ion Systems Machine Learning

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