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AI Research Scientist (2026)

NeuroSphere Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
2 Juli 2026
Deadline
2 Jul 2027

Job Description

Shape the future of artificial intelligence at NeuroSphere Labs, where we're pioneering quantum-entangled neural networks for 2026 and beyond. Join our elite research team in Austin's thriving tech ecosystem to develop next-generation AI systems that will redefine human-machine collaboration. We offer unparalleled resources, including a $50M research budget and partnerships with MIT and Stanford.

This role requires deep expertise in emergent AI paradigms and the ability to translate theoretical breakthroughs into practical applications. You'll work in our state-of-the-art Neuro-Quantum lab, collaborating with Nobel laureates and Turing Award winners to solve humanity's most complex challenges.

Responsibilities

  • Design and implement quantum-enhanced neural architectures for 2026-era AI applications
  • Lead breakthrough research in consciousness simulation and AGI alignment
  • Develop ethical frameworks for autonomous decision-making systems
  • Collaborate with neuromorphic engineering teams on bio-inspired computing
  • Author peer-reviewed publications for Nature AI and Science Robotics
  • mentor postdoctoral researchers in quantum machine learning
  • Secure $5M+ in federal and private research grants

Qualifications

  • PhD in Quantum Computing, Computational Neuroscience, or equivalent with 5+ years research experience
  • Published work in top-tier AI/quantum journals (NeurIPS, ICML, PRL)
  • Expertise in tensor networks and quantum machine learning frameworks
  • Proficiency in Python/C++ with quantum computing libraries (Qiskit, Cirq)
  • Demonstrated ability to lead multi-disciplinary research teams
  • Experience securing government research grants (NSF, DARPA)
  • Deep understanding of AI ethics and existential risk mitigation

Required Skills

Quantum Computing Neural Networks Machine Learning Python C++ Research Ethics AGI Neuromorphic Engineering Tensor Networks

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