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Quantum Computing Research Scientist

Nexus Dynamics
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
20 Mei 2026
Deadline
20 Mei 2027

Job Description

Join Nexus Dynamics at the forefront of technological evolution as we pioneer quantum computing applications for 2026. We're seeking visionary Quantum Computing Research Scientists to develop groundbreaking solutions that will redefine industries. Our Austin-based innovation hub offers unparalleled resources and collaborative freedom to explore uncharted computational territories.

As a key member of our Quantum Research Division, you'll work alongside Nobel laureates and industry pioneers to push the boundaries of quantum algorithms, error correction, and practical implementations. We offer competitive compensation, flexible work arrangements, and a commitment to your professional growth through cutting-edge training and conference opportunities.

Responsibilities

  • Design and implement novel quantum algorithms for optimization and machine learning applications
  • Lead research in quantum error correction techniques for scalable quantum processors
  • Collaborate with hardware teams to develop quantum-classical hybrid computing frameworks
  • Publish peer-reviewed research and present findings at top-tier conferences
  • Secure research grants and patents in quantum computing methodologies
  • Mentor junior researchers and drive cross-functional innovation initiatives

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field (or equivalent experience)
  • 3+ years of hands-on quantum algorithm development experience
  • Proficiency in quantum programming languages (Q#, Qiskit, Cirq)
  • Published research in quantum computing or quantum information theory
  • Deep understanding of quantum decoherence and error correction principles
  • Strong background in high-performance computing and parallel processing
  • Experience with quantum hardware platforms (IBM Quantum, Rigetti, etc.)

Required Skills

Quantum Computing Qiskit Q# Machine Learning Algorithm Design Error Correction High-Performance Computing Python C++ Research Methodology

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