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Quantum Computing Researcher 2026

Nexus Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

Join Nexus Labs at the forefront of technological innovation as we pioneer quantum computing solutions for 2026. We seek a visionary researcher to develop next-gen quantum algorithms and architectures that will redefine computational possibilities. This role offers unparalleled opportunities to shape the future of quantum technology in a collaborative, cutting-edge environment.

Our state-of-the-art facility in San Francisco provides access to industry-leading quantum hardware and a multidisciplinary team of experts. You'll contribute to breakthrough projects with global impact while enjoying competitive compensation and comprehensive benefits.

Responsibilities

  • Design and implement novel quantum algorithms for optimization and machine learning applications
  • Develop error-corrected quantum architectures targeting 2026 scalability milestones
  • Collaborate with hardware engineers to integrate quantum-classical hybrid systems
  • Lead research initiatives in quantum cryptography and secure communication protocols
  • Publish findings in top-tier scientific journals and present at international conferences
  • Mentor junior researchers and drive cross-functional innovation projects

Qualifications

  • PhD in Quantum Computing, Physics, Computer Science, or related field
  • 3+ years of hands-on experience with quantum programming frameworks (Qiskit, Cirq)
  • Published research in quantum algorithm development or quantum error correction
  • Expertise in quantum simulation and hardware-software co-design
  • Strong background in linear algebra, probability theory, and computational complexity
  • Proven ability to lead complex technical projects and deliver results
  • Excellent communication skills for interdisciplinary collaboration

Required Skills

Quantum Computing Quantum Algorithms Quantum Error Correction Qiskit Cirq Quantum Cryptography Machine Learning Linear Algebra Research Leadership

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