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Job Description
The postdoctoral fellow will work closely with Asst. Prof. Patrick Rebentrost in the intersection of quantum algorithms and optimization, machine learning and/or finance. Sample topics include quantum algorithms for problems in supervised and unsupervised machine learning, algorithmic learning theory, and online learning, and quantum algorithms for financial problems such as portfolio optimization, pricing of financial derivatives, and hedging. The job will include the development of quantum algorithms in these areas, with provable quantum advantages and provable guarantees, when possible, in fault-tolerant models of quantum computation. The job will also include the development of algorithms that are easier to execute on available hardware such as variational and post-variational methods and quantum approximate optimization. Furthermore, the aim is to provide hardware implementations on available devices, comparisons between available hardware providers, and classical simulations of the quantum algorithms. For this purpose, the successful applicant will be granted significant independence concerning their research and receive generous travel funding. They will be expected to contribute to the group, for example by co-supervising student projects and assisting with outreach activities.
Qualifications
• Strong track-record working on technical aspects of quantum algorithms and/or quantum information. Strong candidates with proven expertise in other aspects of quantum computing such as quantum machine learning, near-term quantum algorithms, quantum approximate optimization are encouraged to apply.
• The candidate is expected to have a PhD in electrical engineering, computer science, physics or applied mathematics and a substantial publication record in theoretical quantum computing/information.
• Strong research profile and proven ability to communicate research
