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2026 Quantum AI Research Lead

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

Job Description

Join Nexus Labs at the forefront of 2026's quantum-AI revolution. We're pioneering the next generation of computational intelligence that will redefine global technology landscapes. As our Quantum AI Research Lead, you'll architect breakthrough solutions at the intersection of quantum computing and artificial intelligence, shaping how humanity solves complex challenges from climate modeling to personalized medicine.

This role offers unparalleled resources to explore uncharted territories in quantum machine learning and collaborative AI systems. You'll lead a multidisciplinary team of world-class researchers while interfacing with industry partners and academic institutions to accelerate practical applications of quantum-AI fusion.

Responsibilities

  • Design and execute quantum-AI research frameworks for 2026-scale computational challenges
  • Lead cross-functional teams in developing novel quantum neural network architectures
  • Collaborate with hardware teams to optimize quantum-AI algorithm performance
  • Translate theoretical breakthroughs into patentable quantum-AI methodologies
  • Present findings at premier conferences and publish in top-tier journals
  • Shape Nexus Labs' quantum-AI roadmap and strategic partnerships

Qualifications

  • PhD in Quantum Computing, Machine Learning, or Computational Physics
  • 5+ years in quantum algorithm development or advanced AI research
  • Published work in quantum machine learning or quantum neural networks
  • Expertise in quantum circuit design and error correction methodologies
  • Proficiency with quantum computing platforms (IBM Q, Rigetti, etc.)
  • Strong background in deep learning frameworks (PyTorch, TensorFlow)
  • Experience leading technical teams and managing research budgets

Required Skills

Quantum Computing Machine Learning Neural Networks Quantum Algorithms PyTorch TensorFlow Research Leadership Computational Physics

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