Job Description
Join Nexus Innovations at the forefront of technological revolution as an AI Research Scientist specializing in quantum computing. We're pioneering the next generation of AI systems that will redefine human capabilities by 2026. This role offers unparalleled opportunity to shape the future of machine learning, working with Nobel Prize-winning researchers in our state-of-the-art quantum lab.
Our ideal candidate thrives at the intersection of theoretical physics and advanced AI, developing algorithms that harness quantum parallelism to solve previously impossible computational challenges. You'll collaborate with world-class engineers to implement quantum-inspired neural networks and contribute to breakthroughs in climate modeling, drug discovery, and autonomous systems.
Responsibilities
- Design and implement quantum machine learning algorithms leveraging Qiskit and TensorFlow Quantum
- Develop hybrid quantum-classical models for exponential computational acceleration
- Lead research initiatives in quantum neural networks and quantum-enhanced reinforcement learning
- Publish groundbreaking findings in Nature Physics and IEEE Transactions on Quantum Engineering
- Mentor interdisciplinary teams in quantum computing principles and AI implementation
- Secure $5M+ in research grants through NSF and DARPA proposals
- Architect fault-tolerant quantum computing frameworks for enterprise applications
Qualifications
- PhD in Quantum Computing, Theoretical Physics, or Machine Learning (post-doc preferred)
- 5+ years experience in quantum algorithm development and quantum circuit optimization
- Expertise in Python, C++, and quantum programming languages (Q#, Qiskit)
- Published research in top-tier quantum computing journals (Science, Nature Quantum)
- Deep understanding of quantum entanglement, superposition, and decoherence mitigation
- Proven track record of translating theoretical quantum models into practical implementations
- Experience with quantum hardware providers (IBM Quantum, Rigetti, IonQ)
- Strong background in tensor networks and quantum machine learning frameworks