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Quantum Computing Researcher 2026

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 280.000
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 seek a visionary Quantum Computing Researcher to develop next-gen algorithms and hardware prototypes that will redefine computational boundaries. In this role, you'll collaborate with Nobel laureates and industry disruptors in our Austin R&D hub, leveraging state-of-the-art quantum processors and AI-driven modeling tools. We offer competitive equity packages, unlimited learning stipends, and the opportunity to shape humanity's technological future.

Responsibilities

  • Design and implement quantum algorithms for optimization, cryptography, and machine learning applications
  • Develop error-correction protocols for fault-tolerant quantum systems
  • Lead experimental validation of quantum hardware prototypes in cryogenic environments
  • Collaborate with cross-functional teams to integrate quantum solutions with classical computing frameworks
  • Publish groundbreaking research in top-tier journals and present at international conferences
  • Secure federal and private research funding through compelling proposals

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field (or equivalent experience)
  • Expertise in quantum circuit design and quantum error correction techniques
  • Proficiency with quantum programming frameworks (Qiskit, Cirq, or Q#)
  • Strong background in linear algebra, probability theory, and computational complexity
  • Proven track record of peer-reviewed publications in quantum computing
  • Experience with low-level hardware interfacing and cryogenic systems
  • Demonstrated ability to translate theoretical concepts into practical implementations

Required Skills

Quantum Computing Quantum Algorithms Cryogenic Engineering Qiskit Cirq Quantum Error Correction Machine Learning Cryptography Low-Level Programming

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