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Quantum AI Research Scientist

Nexus Labs 2026
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
23 Mei 2026
Deadline
23 Mei 2027

Job Description

Join Nexus Labs 2026 at the forefront of technological evolution as we pioneer quantum-AI hybrid systems that will redefine computing by 2026. We're seeking visionary researchers to develop next-generation algorithms that bridge quantum mechanics and artificial intelligence. Our Austin innovation hub offers unparalleled resources and collaborative freedom to transform theoretical breakthroughs into practical applications.

As a key member of our elite research division, you'll work with Nobel laureates, quantum hardware pioneers, and AI architects in a culture that values bold experimentation and intellectual curiosity. We offer competitive equity packages, flexible work arrangements, and dedicated R&D budgets.

Responsibilities

  • Design and implement quantum-AI hybrid algorithms for computational optimization problems
  • Lead cross-functional research initiatives in quantum machine learning and neural networks
  • Develop novel error-correction protocols for quantum-AI systems
  • Publish groundbreaking research in top-tier journals and conferences
  • Collaborate with hardware teams to prototype quantum-AI accelerators
  • Secure $2M+ in research grants and patents
  • Mentor junior researchers in emerging quantum-AI methodologies

Qualifications

  • PhD in Quantum Computing, Machine Learning, or related field (or equivalent experience)
  • 3+ years experience with quantum algorithms and quantum circuit design
  • Proficiency in Python, Qiskit, and TensorFlow/PyTorch frameworks
  • Published research in quantum-AI integration or high-impact journals
  • Demonstrated ability to secure research funding or patents
  • Expertise in quantum error correction and fault-tolerant computing
  • Experience with cloud quantum platforms (IBM Quantum, Amazon Braket)

Required Skills

Quantum Computing Machine Learning Quantum Algorithms Python Qiskit TensorFlow Research Methodology Error Correction

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