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Information Technology 🏢 Full Time ⭐️ Verified

Quantum AI Research Scientist

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

Job Description

Join Nexus Labs at the forefront of technological revolution as we pioneer quantum AI systems that will redefine 2026 and beyond. Our Austin-based research center combines cutting-edge quantum computing with advanced neural networks to solve humanity's most complex challenges. We're seeking visionary quantum AI researchers to architect next-generation algorithms, develop error-corrected quantum processors, and create hybrid quantum-classical frameworks. This is your opportunity to shape the technological landscape of tomorrow while working alongside Nobel laureates and Turing Award winners in our state-of-the-art facility overlooking the Colorado River.

Responsibilities

  • Design and implement quantum neural network architectures for 2026-era applications
  • Develop quantum error correction protocols for scalable qubit systems
  • Create hybrid quantum-classical optimization algorithms for logistics and drug discovery
  • Lead cross-functional teams in prototyping quantum AI solutions
  • Publish breakthrough research in Nature/Science journals
  • Collaborate with hardware teams to co-design quantum processors
  • Secure $5M+ in government and private research grants

Qualifications

  • PhD in Quantum Computing, Physics, or AI with 3+ years postdoc experience
  • Published research in quantum machine learning or quantum information theory
  • Expertise in Qiskit, Cirq, or similar quantum programming frameworks
  • Strong background in tensor networks and quantum circuit optimization
  • Experience with superconducting or trapped-ion quantum systems
  • Track record of securing federal research grants (NSF/DOE)
  • Proficiency in Python, TensorFlow, and high-performance computing

Required Skills

Quantum Computing Machine Learning Python Qiskit Quantum Error Correction Neural Networks High-Performance Computing TensorFlow

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