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Information Technology 🏢 Full Time ⭐️ Verified

Quantum Computing Researcher

Nexus Labs
Austin
Estimated Salary
USD 150.000 – USD 220.000
New
Live Update
15 Mei 2026
Deadline
15 Mei 2027

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer quantum computing breakthroughs for 2026. We're seeking a visionary Quantum Computing Researcher to develop next-gen algorithms and solve previously unsolvable computational challenges. Our Austin-based innovation hub offers unparalleled resources to transform theoretical concepts into real-world applications.

As part of our elite R&D team, you'll collaborate with Nobel laureates and industry pioneers to push the boundaries of quantum mechanics. This role offers competitive compensation, cutting-edge lab access, and the opportunity to shape humanity's technological future.

Responsibilities

  • Design and implement quantum algorithms for optimization and simulation problems
  • Conduct advanced research in quantum error correction and decoherence mitigation
  • Develop scalable quantum hardware interfaces and control systems
  • Lead cross-functional teams to translate research into commercial applications
  • Publish breakthrough findings in top-tier journals and conferences
  • Secure federal and private research funding through compelling proposals
  • Mentor junior researchers and contribute to quantum education initiatives

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (or equivalent experience)
  • 3+ years of hands-on quantum algorithm development experience
  • Expertise in quantum programming languages (Q#, Qiskit, Cirq)
  • Published research in peer-reviewed quantum computing journals
  • Deep understanding of quantum mechanics and information theory
  • Proven ability to secure research funding (>$1M preferred)
  • Experience with quantum hardware platforms (IBM Q, Rigetti, IonQ)

Required Skills

Quantum Computing Qiskit Cirq Q# Quantum Algorithms Quantum Error Correction Research Methodology Scientific Publication Quantum Hardware Machine Learning

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