Quantum Machine Learning Scientist (PostDoc)

Job type: 


Application deadline: 

Wednesday, March 31, 2021

Research group: 

The center for advance computing at General Atomics (GA), Energy group, is looking for a highly motivated post-doctoral quantum machine learning (QML) scientist to assist with quantum algorithm development for addressing problems in unsupervised and supervised learning. The scope of algorithms includes quantum autoencoders, quantum kernel methods, and quantum CNN. In addition, the class of variational quantum algorithms for NISQ devices will be emphasized. Domain application areas include signal processing, energy science, and chemistry. Numerical implementation and use of simulators to evaluate algorithms is expected along with potential use of quantum hardware when feasible.

To be successful in this role, the post-doctoral QML scientist will need to be self-motivated, a good communicator, possess a proper quantum physics and ML background, and be capable of working with a team of interdisciplinary scientists.

The post-doctoral QML scientist will be hosted at General Atomics in San Diego, CA. The position is a one-year contract with the potential for renewal.

Required Qualifications:
• Ph.D. in Quantum Physics, Computer Science or related disciplines such as Applied Mathematics or Quantum Chemistry.
• Strong understanding of gate-based quantum computing and quantum information science.
• Experience in the development and deployment of unsupervised and supervised machine learning models.
• Experience programming in python and familiar with one or more of the following APIs: Qiskit, Cirq/TensorFlow-Quantum, PennyLane

Highly Desired Qualifications:
• History of presentations and publications in quantum computing and/or machine learning.
• Experience developing or advancing variational quantum algorithms (e.g., design of ansatz circuits)
• Regular user of ML libraries such as scikit-learn, PyTorch, and Tensorflow.
• Familiarity with modern software development methodologies (e.g., Git, unit testing, etc.)
• Experience with global optimization of black-box functions, e.g., Bayesian optimization methods.