Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work with Global Technology Applied Research (GTAR) center at JPMorgan Chase & Co.. The goal of GTAR is to design and conduct research across multiple frontier technologies, in order to enable novel discoveries and inventions, and to inform and develop next-generation solutions for the firm’s clients and businesses.

We are looking for applied algorithms researchers with (i) strong publication record in quantum-inspired or randomized algorithms for optimization and machine learning and/or (ii) experience developing performant code, including using accelerators like GPUs. Experience in finance is a plus, though no prior familiarity with financial use cases is required.

Job Responsibilities:

We are looking for innovative problem solvers with a passion for advancing the state of the art of quantum-inspired algorithms. The focus of your role will be to:

Advance the field of quantum-inspired and randomized algorithms and their applications to optimization, machine learning and financial use cases Implement the developed algorithms in performant software Provide novel research solutions to problems faced by internal project teams Work with other researchers to document your findings in scientific papers and present them at conferences Contribute to JPMC’s IP by pursuing necessary protections of generated IP

Required qualifications, capabilities, and skills

Ph.D. degree in computer science, physics, math, engineering or related fields, plus to have industry or postdoc experience  Demonstrated research ability in quantum-inspired algorithms or related fields Experience in scientific technical writing Proficiency in Python or C/C++ Experience developing performant codes Strong communication skills and the ability to present findings to a non-technical audience Experience in one or more following domains:  Quantum-inspired algorithms (e.g., dequantized algorithms, tensor networks, novel techniques for Ising solvers) Randomized algorithms (e.g., streaming algorithms, data sketching techniques) Architectural design for efficient implementation (e.g., developing memory efficient sampling techniques, end-to-end implementation of quantum-inspired and randomized algorithms) GPU programming (e.g., CUDA, SYCL)

Preferred qualifications, capabilities, and skills

Preference is given to candidates with strong publication record (example venues include but are not limited to ICML, NeurIPS, ICLR, ISCA, HPCA, STOC, FOCS) No prior familiarity with finance or financial use cases is required Preference given to candidates who includes a link to their Google Scholar or Semantic Scholar profile in their resume.