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Quantum Computing Research Scientist

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 150.000 – USD 250.000
Live Update
19 Mei 2026
Deadline
19 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 Research Scientist to develop next-gen quantum algorithms and systems that will redefine computational boundaries. Collaborate with Nobel laureates and industry disruptors in our state-of-the-art facility overlooking the Bay. Your work will directly impact breakthroughs in materials science, cryptography, and AI optimization.

We offer unparalleled resources, flexible hybrid work arrangements, and a culture that rewards intellectual curiosity. This role includes competitive equity packages and comprehensive wellness benefits designed for the modern innovator.

Responsibilities

  • Design and implement novel quantum algorithms for real-world applications
  • Lead experimental quantum hardware testing and optimization initiatives
  • Develop hybrid quantum-classical computing frameworks
  • Collaborate with cross-functional teams to integrate quantum solutions
  • Publish groundbreaking research in top-tier scientific journals
  • Secure research grants and patents for intellectual property
  • Mentor junior researchers and quantum engineering interns

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 3+ years of hands-on quantum computing experience
  • Proficiency in quantum programming languages (Q#, Quipper, Qiskit)
  • Strong background in quantum error correction techniques
  • Publication record in quantum information science
  • Experience with superconducting or trapped-ion quantum systems
  • Deep understanding of quantum complexity theory
  • Excellent written and verbal communication skills

Required Skills

Quantum Computing Quantum Algorithms Qiskit Q# Quantum Error Correction Superconducting Qubits Trapped-Ion Systems Quantum Cryptography Machine Learning

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