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Quantum Computing Research Scientist

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
16 Mei 2026
Deadline
16 Mei 2027

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking a visionary Research Scientist to develop groundbreaking quantum algorithms and architectures that will redefine computational boundaries. Our Austin-based innovation hub offers unparalleled resources to accelerate your research, including access to state-of-the-art quantum processors and collaborative partnerships with leading academic institutions.

Why Nexus Labs? We're not just preparing for the future—we're actively building it. Our cross-disciplinary teams work in agile sprints to translate theoretical breakthroughs into practical applications across cryptography, material science, and AI optimization. With competitive equity packages and dedicated R&D budgets, your ideas will have the runway to scale.

Responsibilities

  • Design and implement novel quantum algorithms for complex optimization problems
  • Develop error-correction protocols to enhance quantum circuit stability
  • Collaborate with hardware engineers to co-design next-generation quantum processors
  • Lead research initiatives in quantum machine learning applications
  • Publish findings in top-tier journals and present at international conferences
  • Mentor junior researchers and foster cross-functional innovation

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science
  • 3+ years of hands-on experience with quantum programming frameworks (Qiskit, Cirq, or Q#)
  • Expertise in quantum error correction and fault-tolerant systems
  • Strong publication record in quantum information science
  • Experience with high-performance computing architectures
  • Proven ability to translate theoretical concepts into experimental prototypes

Required Skills

Quantum Computing Quantum Algorithms Error Correction Qiskit Cirq Quantum Machine Learning Fault Tolerance

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