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Quantum UX Researcher 2026

NeuroTech Dynamics
Austin
Estimated Salary
USD 140.000 – USD 190.000
Live Update
22 Mei 2026
Deadline
22 Mei 2027

Job Description

Join NeuroTech Dynamics at the forefront of human-computer interaction evolution. We're pioneering quantum-entangled user experience research to design interfaces for the next decade of neural-adjacent technologies. This role blends cognitive neuroscience, AI-assisted behavioral analytics, and speculative design to create intuitive experiences for brain-complement interfaces.

As our Quantum UX Researcher, you'll collaborate with cross-disciplinary teams to decode subconscious user patterns using quantum-encrypted biometric datasets. You'll shape the ethical frameworks governing neuro-interaction while developing predictive models for 2026's ambient computing landscape.

Responsibilities

  • Design and execute quantum-enhanced user studies using neural-entangled testing protocols
  • Develop predictive UX models for brain-complement interfaces using quantum-encrypted biometrics
  • Lead speculative design workshops mapping 2026's ambient computing ecosystem
  • Create ethical frameworks for neuro-interaction technologies
  • Translate quantum cognitive data into actionable design principles
  • Collaborate with quantum algorithm engineers to optimize neural feedback loops

Qualifications

  • PhD in Cognitive Neuroscience or Human-Computer Interaction with quantum computing specialization
  • 5+ years in UX research with demonstrated experience in neural interface projects
  • Proficiency in quantum-encrypted biometric analysis tools
  • Published research on subconscious pattern recognition in AI-augmented environments
  • Expertise in speculative design methodologies for emerging technologies
  • Certification in quantum ethics from accredited institutions
  • Portfolio showcasing neuro-interaction design prototypes

Required Skills

Quantum UX Research Neural Interface Design Cognitive Neuroscience Speculative Design Quantum Ethics Biometric Analytics Predictive Modeling Brain-Computer Interfaces

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