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Quantum Computing Research Scientist

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

Job Description

Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We seek a visionary Quantum Computing Research Scientist to develop next-generation algorithms and hardware systems that will redefine computational boundaries. This role offers unparalleled opportunities to shape the future of artificial intelligence, cryptography, and materials science in our state-of-the-art Austin facility.

As part of our elite innovation team, you'll collaborate with Nobel laureates and industry pioneers to solve humanity's most complex challenges. We offer competitive equity packages, unlimited R&D budgets, and flexible work arrangements designed for peak productivity.

Responsibilities

  • Design and implement quantum algorithms for optimization and machine learning applications
  • Develop error correction protocols for fault-tolerant quantum systems
  • Lead cross-functional research initiatives with materials science and AI teams
  • Publish breakthrough research in peer-reviewed journals and industry conferences
  • Collaborate with hardware engineers to translate theoretical models into practical implementations
  • Mentor junior researchers and contribute to quantum education initiatives

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science from an accredited institution
  • 3+ years of hands-on experience with quantum programming (Qiskit, Cirq, or Q#)
  • Published research in quantum algorithms or quantum information theory
  • Expertise in quantum error correction and fault-tolerant architectures
  • Strong background in linear algebra, probability theory, and computational complexity
  • Proven ability to lead high-impact research projects with measurable outcomes

Required Skills

Quantum Computing Quantum Algorithms Qiskit Cirq Quantum Error Correction Fault-Tolerant Systems Machine Learning Cryptography

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