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Quantum Computing Research Scientist 2026

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer breakthroughs in quantum computing for 2026 and beyond. We're seeking visionary Quantum Computing Research Scientists to decode the next era of computational power. Our interdisciplinary team operates at the intersection of theoretical physics, advanced mathematics, and cutting-edge engineering to solve humanity's most complex challenges. You'll collaborate with Nobel laureates and industry disruptors in a culture that values intellectual curiosity and audacious innovation.

Position benefits include unlimited R&D budget, flexible remote work options, and equity participation in our moonshot projects. This role offers unparalleled opportunities to shape the quantum landscape of the coming decade.

Responsibilities

  • Lead pioneering research in quantum error correction and fault-tolerant architectures
  • Develop novel quantum algorithms for optimization and simulation applications
  • Collaborate with hardware teams to design next-generation qubit systems
  • Publish breakthrough findings in top-tier journals and present at global conferences
  • Secure federal and private research funding for 2026 quantum initiatives
  • Mentor PhD candidates in quantum information science programs

Qualifications

  • PhD in Physics, Computer Science, or related quantum field (2021-2026 graduates encouraged)
  • Expertise in quantum circuit design and quantum machine learning frameworks
  • Publication record in Nature/Science or equivalent high-impact journals
  • Proficiency in quantum programming languages (Q#, Qiskit, Cirq)
  • Experience with cryogenic quantum systems and photonic processors
  • Strong background in topological quantum computing models

Required Skills

Quantum Computing Quantum Algorithms Cryogenic Systems Qiskit Q# Topological Quantum Computing Machine Learning Theoretical Physics

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