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Machine Learning Quantum Computing Jobs in Windsor, CA

Cyber Security Engineer

Bodega Bay, CA · On-site

$156K - $191K/yr

Evaluate Edge Computing Networks and Zero Trust architectures by working with internal and external collaborators. * Apply data modeling, visualization, machine learning, and statistical analysis ...

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Machine Learning Quantum Computing information

See Windsor, CA salary details

$28K

$46.8K

$96.6K

How much do machine learning quantum computing jobs pay per year?

As of Jul 14, 2026, the average yearly pay for machine learning quantum computing in Windsor, CA is $46,761.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,700.00 and $50,500.00 per year, depending on experience, location, and employer.

What is the difference between Machine Learning Quantum Computing vs Data Scientist?

AspectMachine Learning Quantum ComputingData Scientist
Required CredentialsAdvanced degrees in quantum computing, machine learning, or related fieldsDegree in data science, statistics, or computer science
Work EnvironmentResearch labs, tech companies focusing on quantum tech, academiaBusiness environments, tech companies, consulting firms
Industry UsageEmerging quantum tech industry, research institutionsFinance, healthcare, marketing, e-commerce
Common Search/ComparisonQuantum algorithms, quantum machine learningData analysis, predictive modeling

Machine Learning Quantum Computing specialists focus on developing algorithms that leverage quantum mechanics to enhance machine learning tasks, often requiring advanced knowledge of quantum physics. Data Scientists analyze and interpret large datasets using traditional machine learning techniques. While both roles involve machine learning, the former emphasizes quantum computing applications, whereas the latter centers on data analysis in conventional computing environments.

What are the key skills and qualifications needed to thrive as a Machine Learning Quantum Computing Specialist, and why are they important?

To thrive in Machine Learning Quantum Computing, you need strong foundations in quantum mechanics, linear algebra, and advanced machine learning concepts, typically supported by a degree in physics, computer science, or a related field. Familiarity with quantum programming languages (such as Qiskit or Cirq), cloud-based quantum platforms, and proficiency in Python are usually required, alongside experience with relevant certifications or coursework. Strong problem-solving skills, adaptability, and effective collaboration are vital soft skills in this interdisciplinary field. These competencies are crucial for driving innovation and bridging the gap between quantum computing and practical machine learning applications.

How do professionals in Machine Learning Quantum Computing typically collaborate with interdisciplinary teams?

Professionals in Machine Learning Quantum Computing often work closely with experts in physics, computer science, and engineering. Collaboration usually involves translating quantum concepts for machine learning specialists and vice versa, ensuring that algorithms are both theoretically sound and practically implementable on quantum hardware. Regular meetings, code reviews, and knowledge-sharing sessions are standard, as interdisciplinary insight is crucial for advancing research and developing scalable solutions. Effective communication and a willingness to learn from other domains are essential for success in these teams.

What is Machine Learning Quantum Computing?

Machine Learning Quantum Computing is an interdisciplinary field that combines principles of quantum computing with machine learning techniques. It aims to leverage the computational power of quantum computers to enhance the performance of machine learning algorithms, potentially solving complex problems more efficiently than classical computers. This area includes developing quantum algorithms for tasks such as classification, clustering, and optimization, as well as using machine learning to improve quantum hardware and error correction. Researchers expect that, as quantum hardware matures, this field could revolutionize data analysis, cryptography, and scientific discovery.
What cities near Windsor, CA are hiring for Machine Learning Quantum Computing jobs? Cities near Windsor, CA with the most Machine Learning Quantum Computing job openings:

Electrochemical Engineering Postdoctoral Scholar

ED-Energy Storage & Distributed R

Bodega Bay, CA

Other

Posted 24 days ago


Job description

In this exciting role, you will simulate mathematically fuel cell systems and other related electrochemical devices and their components under both steady state and transient operation in The Energy Conversion Group at Energy Technologies and Systems Division of the Energy Technologies Area.  Your work will entail model development, validation, and execution including collaboration with research partners, to verify and explain predicted trends seen in experimental data.  The model should identify critical barriers and provide strategies to enable performance optimization and durability mitigation. In addition to cell level modeling, particular emphasis will be on understanding multi-ion transport and durability stressors including structure/function relationships across multiple time and length scales. Additionally, incorporating AI/ML into the multiphysics models via surrogate models or other data-driven methods will be a focus in this position. 

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:

  • Develop and refine mathematical models to examine multidimensional, multiphysics, transport within an electrochemical cell including its various constitutive layers (e.g., porous transport layers, gas-diffusion and microporous layers, membrane, and catalyst layers). 

  • Analyze the results to examine limiting factors in performance as well as identify areas of deficiency in the model and propose new mathematical constructs to deal with them.

  • Pursue microstructural simulations of transient phenomena within single components.

  • Use AI/ML approaches (e.g., physics-informed neural networks) for surrogate model development   

  • Validate model activities and comparison of simulation to experimental data for both input parameters and output results.     

  • Publish original research in peer-reviewed journals; contribute to scientific publications; present research through talks and posters at conferences, workshops, and multi-investigator meetings.

  • Adhere with the Berkeley Lab and ETA safety requirements.

  • Work on meeting milestones and reporting them to DOE and industrial sponsors

  • Collaborate and work with a team of researchers from diverse backgrounds, and interface with research teams from across industry, academia, and national laboratories

 

Additional Responsibilities as needed:

  • Work on experimental characterization of cell performance and measurement of component properties. 

  • Interact with the LBNL fuel cell and electrochemistry community (with extensive experience in batteries, modeling of batteries and fuel cells, electrode material synthesis, spectroscopy, detailed diagnostics, and cell design) to aid in electrochemistry research.    

  • Participate in professional society activities.

We are looking for:

  • PhD in chemical engineering, mechanical engineering, applied physics, or closely related field.  

  • Experience with mathematical modeling (i.e., continuum modeling) including in the application of transport phenomena in fuel cells or related devices and at various scales. 

  • Familiarity with high performance computing and code development and use including the use of AI/ML and surrogate model development for multiscale analysis  

  • Excellent communication skills, both oral and written as well as technical writing.  

  • Ability to learn rapidly and integrate new fields to demonstrate creative problem-solving skills 

  • Ability and willingness to work in a team environment and collaborate with researchers from various backgrounds

  • Ability to work as an independent researcher with a high level of scientific judgment and initiative. 

  • Knowledge of electrochemistry and related diagnostic methods and materials for fuel cells (both low and intermediate temperatures).

  • Knowledge of constitutive relations and continuum theories.

 

Desired skills/knowledge:

  • Demonstrated ability to take initiative for tackling cross-disciplinary research problems from initiation to meaningful conclusion.

  • Experience with electrochemistry, hydrogen fuel cells, and transport phenomena.

  • Demonstrated strong experience with finite-element methods and Comsol.

  • Familiarity with machine learning and associated big data techniques.

For consideration, please apply with the following application materials:

  • Cover Letter - Describe your interest in this position and the relevance of your background.

  • Curriculum Vitae (CV) or Resume.

Additional information:

  • Appointment type: This is a full-time 2 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 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.

  • Salary range: The monthly salary range for this position is $6,891 / mo - $7,609.00 / mo  and is expected to start at $6,891 / mo 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 and/or related research 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 will be primarily performed on-site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. 

  • 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.