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Quantum Computing Research Lead

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

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking a visionary Research Lead to architect next-gen quantum algorithms and drive breakthroughs in computational science. This role offers unparalleled opportunity to shape the future of artificial intelligence, cryptography, and materials science while collaborating with Nobel laureates and industry pioneers.

Our state-of-the-art facility in San Francisco provides an immersive environment where your expertise will directly impact global tech infrastructure. You'll lead a multidisciplinary team of quantum physicists, software engineers, and domain experts to solve previously unsolvable computational challenges.

Responsibilities

  • Design and implement quantum algorithms for machine learning optimization
  • Lead research in quantum error correction and fault-tolerant systems
  • Collaborate with hardware teams to co-design quantum processors
  • Develop quantum-resistant cryptographic protocols for 2026 security standards
  • Publish breakthrough research in peer-reviewed journals and conferences
  • Mentor junior researchers and foster cross-functional innovation
  • Secure patents for novel quantum computing methodologies

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science
  • 5+ years of quantum algorithm development experience
  • Expertise in Qiskit, Cirq, or equivalent quantum SDKs
  • Published research in quantum machine learning or cryptography
  • Deep understanding of quantum decoherence and error correction
  • Proven track record of leading high-stakes R&D projects
  • Strong background in linear algebra and quantum mechanics
  • Experience with cloud quantum computing platforms (IBM Quantum, Amazon Braket)

Required Skills

Quantum Computing Quantum Algorithms Qiskit Cirq Cryptography Machine Learning Fault-Tolerant Systems Research Leadership

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