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Quantum Computing Research Scientist 2026

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
14 Mei 2026
Deadline
14 Mei 2027

Job Description

Join Nexus Quantum Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking visionary Research Scientists to develop groundbreaking algorithms, optimize quantum hardware performance, and solve previously unsolvable computational challenges. Our state-of-the-art facility in San Francisco offers unparalleled resources and collaborative environment where your innovations will shape the future of global computing.

Responsibilities

  • Design and implement novel quantum algorithms for complex problem-solving
  • Optimize qubit coherence and gate fidelity in next-generation quantum processors
  • Lead cross-functional teams to integrate quantum solutions with classical computing frameworks
  • Publish research in top-tier journals and present at international quantum conferences
  • Collaborate with hardware engineers to prototype and validate quantum circuits
  • Develop error-correction techniques for fault-tolerant quantum systems
  • Secure patents for proprietary quantum methodologies and architectures

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (MS with 5+ years experience considered)
  • Proven expertise in quantum programming (Q#, Qiskit, Cirq) and algorithm design
  • Deep understanding of quantum mechanics principles and quantum information theory
  • Experience with superconducting qubits, trapped ions, or photonic quantum systems
  • Strong background in machine learning and classical optimization techniques
  • Publication record in quantum computing or related fields
  • Ability to secure government grants and industry partnerships
  • Excellent communication skills for technical and non-technical audiences

Required Skills

Quantum Computing Qiskit Q# Machine Learning Quantum Algorithms Superconducting Qubits Quantum Error Correction Python C++ Tensor Networks

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