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Quantum Computing Research Scientist

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
1 Juli 2026
Deadline
1 Jul 2027

Job Description

Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. Our Austin-based innovation hub is seeking a visionary Quantum Computing Research Scientist to develop next-generation algorithms and hardware prototypes. You'll collaborate with Nobel laureates and industry pioneers in our state-of-the-art labs, pushing the boundaries of computational physics while solving real-world challenges in cryptography, materials science, and AI optimization. This role offers unparalleled resources and mentorship for those ready to shape humanity's technological future.

Responsibilities

  • Design and implement novel quantum algorithms for optimization problems
  • Develop error-correction protocols for fault-tolerant quantum systems
  • Lead experimental validation of quantum hardware prototypes
  • Collaborate with cross-functional teams to translate quantum solutions into commercial applications
  • Publish groundbreaking research in peer-reviewed journals and industry whitepapers
  • Secure federal and private research funding through compelling proposals
  • Mentor junior researchers and contribute to quantum education initiatives

Qualifications

  • PhD in Physics, Computer Science, or related quantum field
  • 3+ years of hands-on quantum computing research experience
  • Expertise in quantum algorithms and error correction techniques
  • Proficiency with quantum programming frameworks (Qiskit, Cirq, or Q#)
  • Strong publication record in quantum computing or condensed matter physics
  • Experience with cryogenic quantum hardware systems
  • Demonstrated ability to secure research grants and funding
  • Passion for ethical AI development and quantum democratization

Required Skills

Quantum Computing Quantum Algorithms Error Correction Cryogenics Qiskit Cirq Quantum Hardware Cryptography Machine Learning

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