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Quantum Computing Research Scientist

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

Job Description

Shape the future of technology at Nexus Quantum Labs as we pioneer the next frontier of computational power. In 2026, quantum computing transitions from theory to real-world applications, and we're seeking visionary researchers to lead this revolution. Join our interdisciplinary team of physicists, engineers, and computer scientists in developing quantum algorithms that solve previously impossible problems in cryptography, drug discovery, and climate modeling. You'll collaborate with industry giants and academic partners to deploy quantum systems at scale, transforming how humanity tackles global challenges.

Responsibilities

  • Design and implement novel quantum algorithms for practical industrial applications
  • Lead research initiatives in quantum error correction and fault-tolerant computing
  • Collaborate with hardware teams to optimize quantum processor performance
  • Publish breakthrough research in top-tier journals and conferences
  • Develop quantum software frameworks for enterprise integration
  • Mentor junior researchers and drive cross-functional innovation
  • Secure patents and intellectual property for quantum methodologies

Qualifications

  • PhD in Physics, Computer Science, or related field (or equivalent experience)
  • 3+ years in quantum computing research with published papers
  • Proficiency in quantum programming languages (Qiskit, Cirq, Quil)
  • Expertise in quantum algorithms and complexity theory
  • Experience with superconducting or trapped-ion quantum systems
  • Strong background in linear algebra and quantum mechanics
  • Ability to translate theoretical concepts into practical implementations
  • Track record of securing research grants or industry funding

Required Skills

Quantum Computing Quantum Algorithms Qiskit Cirq Superconducting Qubits Quantum Error Correction Theoretical Physics Machine Learning

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