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Information Technology 🏢 Full Time ⭐️ Verified

Quantum Computing Research Scientist

Nexus Labs
Austin
Estimated Salary
USD 185.000 – USD 275.000
Live Update
16 Mei 2026
Deadline
16 Mei 2027

Job Description

Join Nexus Labs at the forefront of 2026's technological revolution as we pioneer quantum computing applications that will redefine industries. Our Austin-based innovation hub is seeking visionary Quantum Computing Research Scientists to develop next-generation algorithms and solve previously insurmountable computational challenges. You'll collaborate with Nobel laureates and industry pioneers in an environment where curiosity fuels breakthroughs.

We offer competitive equity packages, unlimited R&D budgets, and access to our state-of-the-art quantum annealing facilities. This is your opportunity to shape the computational landscape of the next decade.

Responsibilities

  • Design and implement quantum algorithms for optimization and machine learning applications
  • Lead cross-functional teams in prototyping 2026-ready quantum solutions
  • Publish breakthrough research in peer-reviewed journals and industry whitepapers
  • Develop error mitigation strategies for near-term quantum processors
  • Collaborate with hardware teams to co-design quantum-classical hybrid systems
  • Secure $1M+ in quantum computing research grants
  • Mentor junior researchers in quantum information theory

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (or equivalent experience)
  • 3+ years of hands-on quantum algorithm development experience
  • Proficiency with quantum programming frameworks (Qiskit, Cirq, or Q#)
  • Published research in quantum machine learning or optimization
  • Demonstrated ability to secure government or corporate research funding
  • Expertise in at least one classical ML framework (TensorFlow/PyTorch)
  • Experience with quantum error correction protocols

Required Skills

Quantum Computing Qiskit Cirq Machine Learning Quantum Algorithms Error Correction Research TensorFlow PyTorch

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