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Quantum AI Research Scientist

Nexus Quantum Labs
Austin
Estimated Salary
USD 180.000 – USD 280.000
Live Update
21 Mei 2026
Deadline
21 Mei 2027

Job Description

Shape the future at Nexus Quantum Labs, where we're pioneering the intersection of quantum computing and artificial intelligence. Join our elite team developing next-gen AI systems leveraging quantum supremacy to solve previously impossible computational challenges. This role offers unprecedented opportunities to contribute to breakthrough technologies that will define the 2026 tech landscape.

We're seeking visionary researchers to push boundaries in quantum neural networks, hybrid quantum-classical algorithms, and fault-tolerant computing systems. You'll collaborate with Nobel laureates and industry disruptors in our state-of-the-art Austin facility, working on projects with global impact across healthcare, climate modeling, and materials science.

Responsibilities

  • Design and implement quantum machine learning algorithms for complex optimization problems
  • Develop hybrid quantum-classical AI frameworks for real-world deployment
  • Lead research initiatives in quantum neural networks and quantum-enhanced deep learning
  • Collaborate with hardware teams to co-design quantum computing architectures
  • Publish breakthrough research in top-tier journals and conferences
  • Drive innovation in quantum error correction and fault-tolerant systems

Qualifications

  • PhD in Quantum Computing, Physics, Computer Science, or related field
  • Expertise in quantum programming languages (Q#, Qiskit, Cirq) and frameworks
  • Proven track record of published research in quantum machine learning
  • Deep understanding of quantum algorithms and complexity theory
  • Experience with large-scale distributed computing systems
  • Strong background in AI/ML frameworks (TensorFlow, PyTorch)
  • Excellent communication skills for technical and non-technical audiences

Required Skills

Quantum Computing Machine Learning Q# Qiskit AI Research Quantum Algorithms Neural Networks Fault-Tolerant Computing

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