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Quantum Computing Research Scientist

Nexus Innovations Inc.
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
11 Mei 2026
Deadline
11 Mei 2027

Job Description

Shape the Future: Join Nexus Innovations' 2026 Quantum Revolution

We're pioneering breakthroughs in quantum computing that will redefine industries by 2026. As a Quantum Computing Research Scientist, you'll lead cutting-edge projects in error correction, quantum algorithms, and hardware optimization. Our state-of-the-art lab in San Francisco offers unparalleled resources to transform theoretical concepts into practical applications.

Why Nexus Innovations?

• Collaborate with Nobel laureates and industry pioneers
• Access to quantum processors from IBM, Google, and D-Wave
• Flexible hybrid work arrangements with competitive equity packages
• Dedicated R&D budget of $50M for 2026 initiatives

Responsibilities

  • Design and implement quantum algorithms for optimization and simulation problems
  • Lead research in quantum error correction and fault-tolerant systems
  • Collaborate with hardware teams to develop quantum processor architectures
  • Publish peer-reviewed papers and present at conferences like QIP and IEEE
  • Develop quantum machine learning models for real-world applications
  • Secure $2M+ in NSF/DARPA grants for 2026 quantum projects
  • Mentor junior researchers in quantum computing principles

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 3+ years of hands-on quantum programming (Qiskit, Cirq, or Q#)
  • Expertise in quantum error correction and fault tolerance
  • Publication record in top-tier journals (Nature, Science, PRL)
  • Experience with superconducting or trapped-ion quantum systems
  • Strong background in linear algebra and quantum information theory
  • Proven ability to secure government research funding
  • Excellent written and verbal communication skills

Required Skills

Quantum Computing Qiskit Cirq Quantum Algorithms Error Correction Quantum Hardware Machine Learning Linear Algebra Research Scientific Publication

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