Postdoc: Thermodynamics of Distributed Computation

SANTA FE INSTITUTE LIBRARY; Santa Fe (NM); On-site; USD 60,000 - 80,000; Full time

Benefits offered by this job

J1 visa sponsorship

Job summary

The Santa Fe Institute in Santa Fe, NM, invites applications for a two-year, full-time postdoctoral fellowship in physics or computer science to explore the thermodynamic cost of distributed computation across digital circuits, neural networks, and human brains.

The successful candidate will work with PI David Wolpert on projects using mismatch cost (MMC) to relate network topology to cost, speed, robustness, and computational power, scaling MMC to large state spaces.

Qualifications

• Ph.D. in Physics or Computer Science by start date.
• Background in stochastic thermodynamics or theoretical computer science.
• Familiarity with Monte Carlo methods and uncertainty quantification.
• Experience with advanced mathematical modeling and computational analysis.

Responsibilities

• Collaborate with PI and team to advance the project through research and publications.
• Develop and apply mismatch-cost theory and large-state-space techniques.
• Design, train, and analyze distributed computational systems to relate topology to cost, robustness, and speed.
• Investigate how hardness of problems relates to thermodynamic cost.

Skills

distributed computation

programming

mathematical modeling

team collaboration

Education

Ph.D. in Physics

Ph.D. in Computer Science

Tools

Monte Carlo methods

Tensor networks

Uncertainty quantification

Backward differential equations

Job description

The Santa Fe Institute in Santa Fe, NM, invites applications for a two-year, full-time postdoctoral fellowship in physics or computer science to explore the thermodynamic cost of distributed computation across digital circuits, neural networks, and human brains.

The successful candidate will work with PI David Wolpert on projects using mismatch cost (MMC) to relate network topology to cost, speed, robustness, and computational power, scaling MMC to large state spaces.