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How much do mathematical optimization postdoc jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for mathematical optimization postdoc in the United States is $22.32, according to ZipRecruiter salary data. Most workers in this role earn between $19.47 and $24.76 per hour, depending on experience, location, and employer.

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 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 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.
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What job categories do people searching Mathematical Optimization Postdoc jobs look for? The top searched job categories for Mathematical Optimization Postdoc jobs are:
Mathematical Optimization Postdoc jobs near you
Infographic showing various Mathematical Optimization Postdoc job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 86% Full Time, 8% Part Time, and 5% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $46,417 per year, or $22.3 per hour.

Quantum Computing Simulation Postdoctoral Fellow

Lawrence Berkeley National Laboratory

Berkeley, CA • On-site

$8.5K - $9.9K/mo

Full-time

Medical, Retirement, PTO

Re-posted 3 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 120 rated laboratories

Job description

The National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher - Quantum Computing Simulation postdoctoral fellow to join NERSC's Quantum team to help develop algorithms for simulating magnetic materials, perform classical simulations at scale on the upcoming Doudna supercomputer, and running circuits on quantum hardware. You'll collaborate with NERSC staff and scientists, and partners at LLNL and Quera to analyze performance, perform resource estimation, and validate algorithms on hardware.
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:
  • Develop algorithms for magnetic materials: dynamics
  • Analyze and optimize quantum-related workflows, including classical simulation of quantum systems.
  • Develop and apply advanced workflow capabilities such as on-system classical simulations
  • Publish and present results in peer-reviewed venues and conferences. Engage in community knowledge-sharing (e.g. tutorials for the NERSC user base).
  • Validate results on quantum hardware

We are looking for:
  • PhD awarded within the last five years in Physics, Computational Chemistry, Computational Science, Applied Mathematics, or a related field.
  • Programming experience in one or more of: Python, C++, Fortran, Julia.
  • Ability to work effectively on interdisciplinary teams.
  • Clear written and oral communication with a record of leading and reporting results.
  • Knowledge of quantum computing algorithms.

Desired skills/knowledge:
  • Familiarity with tensor network methods.
  • Experience programming GPUs.
  • Experience implementing, optimizing, or integrating quantum libraries such as Itensor, CUDA-Q, Qiskit, or PennyLane.
  • Experience debugging and profiling distributed-memory parallel applications.
  • Knowledge of Git and modern software practices such as unit testing, CI/CD, and collaborative development.

Additional information:
  • Application date: Priority consideration will be given to candidates who apply by August 8, 2026. Applications will be accepted until the job posting is removed.
  • Appointment type: This is a full-time, 3 years, 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 2 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: This position requires substantial on-site presence, but is eligible for a flexible work mode, and hybrid schedules may be considered. Hybrid work is a combination of performing work on-site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA and some telework. Individuals working a hybrid schedule must reside within 150 miles of Berkeley Lab. Work schedules are dependent on business needs.
  • 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 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.

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