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Quantum Computing Research Scientist

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
13 Mei 2026
Deadline
13 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 seek a visionary Quantum Computing Research Scientist to develop groundbreaking algorithms and hardware prototypes that will redefine computational boundaries. Our state-of-the-art facility in San Francisco offers unparalleled resources for exploring quantum supremacy, error correction, and real-world applications in cryptography, materials science, and AI. If you're passionate about shaping the future of technology, this is your opportunity to make history.

Responsibilities

  • Design and implement novel quantum algorithms for complex computational problems
  • Lead experimental research on quantum hardware optimization and error mitigation
  • Collaborate with cross-functional teams to integrate quantum solutions with classical systems
  • Publish findings in leading scientific journals and present at industry conferences
  • Secure external funding through grant proposals and industry partnerships
  • Mentor junior researchers and drive innovation in quantum software development

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 3+ years of hands-on experience with quantum computing frameworks (Qiskit, Cirq, or Q#)
  • Expertise in quantum error correction and fault-tolerant architectures
  • Strong publication record in quantum information science
  • Proficiency in high-performance computing environments and parallel programming
  • Deep understanding of quantum mechanics principles and their practical applications
  • Ability to translate theoretical concepts into scalable experimental prototypes

Required Skills

Quantum Computing Quantum Algorithms Quantum Hardware Error Correction Qiskit Cirq Fault Tolerance Quantum Cryptography High-Performance Computing Machine Learning

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