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Mathematical Optimization Postdoc Jobs in Berkeley, CA

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Lead Research Scientist

Foster City, CA · On-site

$215K/yr

... optimization, risk analysis, and derivative pricing. This leadership role offers a unique ... mathematics, engineering, physics or equivalent field. • 5+ years of postdoctoral research ...

Visa

Lead Research Scientist

Foster City, CA

$215K/yr

... optimization, risk analysis, and derivative pricing. This leadership role offers a unique ... engineering, mathematics, engineering, physics or equivalent field. 5+ years of postdoctoral ...

All JobsMathematical Optimization Postdoc JobsMathematical Optimization Postdoc Jobs in Berkeley, CA

Mathematical Optimization Postdoc information

See Berkeley, CA salary details

$6

$27

$35

How much do mathematical optimization postdoc jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for mathematical optimization postdoc in Berkeley, CA is $27.32, according to ZipRecruiter salary data. Most workers in this role earn between $23.85 and $30.34 per hour, depending on experience, location, and employer.

What is a mathematical optimization postdoc?

A Mathematical Optimization Postdoc is a researcher who has completed their PhD and is engaged in advanced research focused on mathematical optimization. This field involves developing and analyzing algorithms and mathematical models to find the best solutions to complex problems, often under constraints. Postdocs in this area typically work at universities, research institutes, or in industry, collaborating with other scientists and publishing their findings. Their work may be applied to areas such as logistics, machine learning, finance, or engineering. The position is usually temporary, lasting from one to three years, and serves as a stepping stone to permanent academic or industry roles.

What are some common challenges faced by mathematical optimization postdocs when transitioning from academic research to industry projects?

Mathematical Optimization Postdocs often find the transition to industry projects challenging due to differences in project timelines, the need for practical and scalable solutions, and collaboration with interdisciplinary teams. In industry, optimization problems may be less theoretically defined and require rapid prototyping, frequent communication with stakeholders, and adaptability to changing business needs. Developing strong communication skills and learning to balance rigorous research with practical constraints are key to succeeding in this environment.

What are the key skills and qualifications needed to thrive as a mathematical optimization postdoc, and why are they important?

To thrive as a Mathematical Optimization Postdoc, you need an advanced degree (typically a PhD) in mathematics, operations research, or a related field, with a deep understanding of optimization theory and algorithms. Familiarity with programming languages such as Python, MATLAB, or C++, and experience with optimization software like Gurobi or CPLEX, are commonly required. Strong analytical thinking, problem-solving abilities, and effective collaboration and communication skills set outstanding candidates apart. These skills are crucial for conducting innovative research, publishing results, and contributing to interdisciplinary projects in academic or industry settings.

What is the difference between Mathematical Optimization Postdoc vs Operations Research Analyst?

AspectMathematical Optimization PostdocOperations Research Analyst
Required credentialsPhD in mathematics, operations research, or related fieldBachelor's or master's degree in operations research, mathematics, or engineering
Work environmentAcademic research, university labs, research institutesCorporate, government agencies, consulting firms
Employer and industry usageUniversities, research institutionsBusinesses, government, consulting
Common search intentResearch, academic positions, postdoctoral opportunitiesApplying optimization techniques in industry, problem-solving roles

The Mathematical Optimization Postdoc primarily focuses on academic research and advancing theoretical methods in optimization, often within universities or research institutions. In contrast, Operations Research Analysts apply these techniques in practical industry settings to solve real-world problems. While both roles require strong analytical skills, the postdoc emphasizes research and publication, whereas the analyst role centers on implementation and operational decision-making.

What job categories do people searching Mathematical Optimization Postdoc jobs in Berkeley, CA look for?

The top searched job categories for Mathematical Optimization Postdoc jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Mathematical Optimization Postdoc jobs?

Cities near Berkeley, CA with the most Mathematical Optimization Postdoc job openings:

Mathematical Optimization Postdoc jobs near you
Infographic showing various Mathematical Optimization Postdoc job openings in Berkeley, CA as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $56,835 per year, or $27.3 per hour.

Postdoctoral Scholar

Berkeley, CA • On-site

Lawrence Berkeley National Laboratory
5 - 10K employees
Learn more about Lawrence Berkeley National Laboratoryexternal

Full-time

Medical, Retirement, PTO

Re-posted 25 days ago

Lawrence Berkeley National Laboratory rating

9.5

Company rating: 9.5 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

2nd of 121 rated laboratories

Job description

Berkeley Lab's Center for Advanced Mathematics for Energy Research Applications (CAMERA) has a new opening for a postdoctoral scholar to develop cutting-edge mathematics and algorithms to analyze complex data from Department of Energy (DOE) experimental facilities.
This role involves research and development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction of 3D+ structure, heterogeneity, and/or dynamics from scattering and/or microscopy data; (2) autonomous analysis and decision making for self-driving and/or human-in-the-loop experiments; (3) computer vision for extracting complex patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms leading to new applications of machine learning and artificial intelligence to analysis of experimental data. Of particular interest will be new approaches for tackling multimodal data, quantifying uncertainty, providing rigorous theoretical guarantees, and modelling complex physics, noise processes, and measurement error. You will work closely with mathematicians, software engineers, physicists, materials scientists, and beamline scientists to implement these new tools on HPC computer architectures and deliver them as user-friendly software to meet DOE experimental facility needs.
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!
Why join Berkeley Lab?
We invest in our employees by offering a total rewards package you can count on:
  • Exceptional health and retirement benefits, including pension or 401K-style plans
  • A culture where you'll belong - we are invested in our teams!
  • In addition to accruing vacation and sick time, we also have a Winter Holiday Shutdown every year.
  • Parental bonding leave (for both mothers and fathers)

You will:
  • Conduct independent and collaborative research to develop new mathematics and algorithms for analyzing complex data from DOE experimental facilities.
  • Develop new mathematical algorithms targeting one or more focus areas: (1) 3D+ reconstruction of structure/heterogeneity/dynamics from scattering and/or microscopy data; (2) autonomous analysis and decision-making for self-driving and/or human-in-the-loop experiments; (3) computer vision for extracting patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms that enable new reliable new applications of machine learning and artificial intelligence to experimental data analysis.
  • Make advances in one or more of: multimodal data fusion/joint inference; uncertainty quantification with realistic noise/measurement error; complex physics- and artifact-aware forward modeling; and theory-grounded guarantees for proposed algorithms.
  • Collaborate with scientific users and experimentalists at DOE experimental facilities to apply the developed software to real datasets and meet their scientific needs.
  • Publish results in peer-reviewed venues, present at conferences/workshops, and contribute to CAMERA's collaborative research activities.

We are looking for:
  • Ph.D. in Applied Mathematics, Computer Science, Physics, or related field.
  • Strong research track record developing advanced mathematical and computational methods for analyzing complex experimental or imaging data.
  • Demonstrated expertise in several of the following areas: inverse problems, statistics, optimization, uncertainty quantification, and/or computer vision/machine learning.
  • Strong foundation in at least one of: numerical linear algebra, Fourier/spectral methods, scientific computing, and/or high-performance computing.
  • Proven ability to publish in peer-reviewed venues and present research at seminars, workshops, and scientific conferences.
  • Excellent written and verbal communication skills, with the ability to contribute effectively to large, collaborative, multidisciplinary projects in a diverse environment.

Desired skills/knowledge:
  • Familiarity with modern machine learning methods and software, including experience applying them to scientific or experimental datasets.
  • Experience collaborating with domain scientists to analyze real experimental data and translate scientific questions into robust, actionable computational approaches.

Additional information:
  • 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: The salary range for this position is $8,570 - $9,935 and is expected to start at $8,570 or above. 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 may be performed on-site, hybrid. The primary location for this role is Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work must be performed within the United States.
  • Union Represented: This position is represented by a union for collective bargaining purposes.

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 finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.

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