Postdoctoral Scholar - Thermodynamic Computing

The Molecular Foundry at Lawrence Berkeley National Laboratory (LBNL) is seeking a Postdoctoral Scholar - Thermodynamic Computing
. This position will help scale thermodynamic computing, a physics-based form of classical computing that uses thermal noise as a resource. The postdoc will use large-scale simulations at NERSC to explore the basic physics of thermodynamic computers and to design computers able to perform state-of-the-art machine-learning tasks. The position is supported by an LDRD and is based at the Molecular Foundry, working with Stephen Whitelam and Aeron Hammack at the Foundry and Corneel Casert at NERSC.

A thermodynamic computer is a collection of fluctuating degrees of freedom coupled so that their natural dynamics performs a calculation. We have shown that nonlinear thermodynamic computers operating out of equilibrium can do machine-learning inference, with projected energy savings of several orders of magnitude over digital neural networks.

We're here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!

• Design and run large-scale simulations of nonlinear, nonequilibrium thermodynamic computers on GPUs at NERSC, using hybrid evolutionary and gradient-based training methods.
• Benchmark the accuracy, speed, and energy cost of thermodynamic computers against digital neural networks on standard machine-learning tasks.
• Determine how design choices, such as network connectivity, nonlinear response functions, and driving protocols, control inference accuracy and energy dissipation.
• Write and maintain documented, reproducible simulation code.
• Publish results in peer-reviewed journals, and present them at conferences and internal meetings.

• PhD in physics, computer science, applied mathematics, or a related field.
• Experience developing numerical simulations of physical systems, or training machine-learning models.
• Proficiency in a scientific programming language, such as Python, C++, Julia, or Fortran.
• Ability to formulate research questions and pursue them independently.
• Strong communication skills both written and verbal with demonstrated ability to communicate results through publications or presentations.
• Experience with GPU computing and large-scale HPC resources.

• Knowledge of statistical mechanics, stochastic processes, or nonequilibrium physics.
• Experience with machine-learning frameworks such as PyTorch or JAX.

• CV
• Cover Letter describing applicant background and interest in the position
• Publication List

• Application date: Priority consideration will be given to candidates who apply by September 4, 2026
  . Applications will be accepted until the job posting is removed.
• Appointment type: This is a full-time 2-year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.
• Salary range: This position is represented by a union for collective bargaining purposes. The salary range for this position is $99,192 - $110,808. Postdoctoral positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral experience.
• Background check: This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
• Work modality: Work will be primarily performed at:
  
  Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. A REAL  other acceptable form of identification is required to access Berkeley Lab sites (for more information ).

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