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Temporary Machine Learning Postdoc Jobs in California

Postdoctoral Scholar

Bodega Bay, CA · On-site

$69K - $107K/yr

Deploy artificial Intelligence and machine learning to accelerate first-principles computation and ... Postdoctoral positions are paid on a step schedule per union contract and salaries will be ...

Showing results 21-40

Temporary Machine Learning Postdoc information

What is a temporary machine learning postdoc?

A Temporary Machine Learning Postdoc is a fixed-term research position, typically held at a university or research institution, focused on advancing knowledge and techniques in machine learning. Postdoctoral researchers in this role work on specific projects, often collaborating with faculty, graduate students, or industry partners. The position is designed to provide advanced training and research experience after earning a PhD, usually lasting from several months to a couple of years. Temporary postdocs may contribute to publishing academic papers, developing algorithms, and mentoring students, while preparing for longer-term academic or industry careers.

What skills and qualifications are needed to thrive as a temporary machine learning postdoc?

To thrive as a Temporary Machine Learning Postdoc, you need a PhD in a relevant field, a solid grasp of machine learning theory, and strong programming skills (often in Python or R). Experience with tools such as TensorFlow, PyTorch, and high-performance computing environments, as well as a record of peer-reviewed research, is typically required. Strong analytical thinking, collaboration, and effective communication help you stand out in this research-intensive role. These skills are essential for advancing cutting-edge research, publishing impactful findings, and contributing to interdisciplinary projects.

What types of projects and collaborations can a temporary machine learning postdoc expect to engage in?

A Temporary Machine Learning Postdoc typically works on cutting-edge research projects, often contributing to ongoing studies or initiating novel investigations within the field. Collaboration is common, both within their immediate research group and with interdisciplinary teams, such as data scientists, domain experts, or industry partners. Postdocs may also mentor graduate students, present findings at conferences, and publish papers, gaining valuable experience that can lead to academic or industry roles. The environment is fast-paced and research-driven, offering opportunities for professional growth and expanding one's research portfolio.

What is the difference between Temporary Machine Learning Postdoc vs Data Scientist?

AspectTemporary Machine Learning PostdocData Scientist
CredentialsPhD in Computer Science, Data Science, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; often requires experience
Work EnvironmentAcademic or research institutions, labsCorporate, tech companies, startups
Employer & Industry UsageUniversities, research centersBusiness, technology, finance, healthcare
Search & Comparison IntentUnderstanding research-focused roles, academic opportunitiesIndustry roles, applied data analysis, business impact

The Temporary Machine Learning Postdoc is primarily research-oriented, often in academic or research settings, requiring a PhD. In contrast, a Data Scientist typically works in industry, applying data analysis and machine learning to solve business problems, often with a Bachelor's or Master's degree. Both roles involve machine learning skills but differ in environment, focus, and experience level.

What are the most commonly searched types of Machine Learning Postdoc jobs in California?

The most popular types of Machine Learning Postdoc jobs in California are:

What are popular job titles related to Temporary Machine Learning Postdoc jobs in California?

For Temporary Machine Learning Postdoc jobs in California, the most frequently searched job titles are:

What job categories do people searching Temporary Machine Learning Postdoc jobs in California look for?

The top searched job categories for Temporary Machine Learning Postdoc jobs in California are:

What cities in California are hiring for Temporary Machine Learning Postdoc jobs?

Cities in California with the most Temporary Machine Learning Postdoc job openings:

Postdoctoral Scholar - Thermodynamic Computing

Bodega Bay, CA • On-site

$99K - $110K/yr

Full-time

Posted 13 days ago


Key responsibilities

  • Design and run large-scale simulations of nonlinear, nonequilibrium thermodynamic computers on GPUs at NERSC.

  • Benchmark the accuracy, speed, and energy cost of thermodynamic computers against digital neural networks on standard machine-learning tasks.

  • Write and maintain documented, reproducible simulation code.


Job description

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!

You will:

  • 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.

We are looking for:

  • 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.

Desired skills/knowledge:

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

Required Application Materials:

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

Additional information:

  • 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 ID or other acceptable form of identification is required to access Berkeley Lab sites (for more information click here).

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov

Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law.

Misconduct Disclosure Requirement: As a condition of employment, the final candidate who accepts an offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; or have filed an appeal of a finding of substantiated misconduct with a previous employer. For additional information, click here.