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Quantum Computing Research Scientist

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
3 Juni 2026
Deadline
3 Jun 2027

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer quantum computing breakthroughs that will redefine industries by 2026. We're seeking visionary Quantum Computing Research Scientists to develop next-generation algorithms and solve previously unsolvable computational challenges. Our state-of-the-art Austin facility offers unparalleled resources for groundbreaking research in quantum supremacy, error correction, and hybrid quantum-classical systems. Collaborate with Nobel laureates and lead projects with real-world impact in cryptography, drug discovery, and climate modeling.

Responsibilities

  • Design and implement novel quantum algorithms for practical applications
  • Develop quantum error correction protocols for scalable systems
  • Lead cross-functional teams in translating theoretical models into functional prototypes
  • Publish high-impact research in peer-reviewed journals and industry conferences
  • Secure federal and private research funding through compelling proposals
  • Mentor junior researchers in quantum mechanics and computational theory
  • Collaborate with hardware teams to optimize quantum-processor interactions

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 3+ years of hands-on quantum algorithm development experience
  • Expertise in quantum programming languages (Qiskit, Cirq, Q#)
  • Published research in quantum computing or quantum information theory
  • Proficiency in high-performance computing environments
  • Strong background in linear algebra and complex analysis
  • Experience with quantum simulation frameworks (QuEST, QASM)
  • Demonstrated ability to secure research funding

Required Skills

Quantum Computing Quantum Algorithms Qiskit Cirq Quantum Error Correction High-Performance Computing Quantum Cryptography Linear Algebra Research Methodology Q# Machine Learning

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