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Machine Learning Physics Postdoc Jobs (NOW HIRING)

KSB, Inc.

Machine Learning Engineer

Grovetown, GA · On-site

You'll build the data foundation that powers this work, implement and train models that bridge physics-based simulation with modern machine learning, and work closely with an experienced technical ...

Ksb

Machine Learning Engineer

Grovetown, GA

You'll build the data foundation that powers this work, implement and train models that bridge physics-based simulation with modern machine learning, and work closely with an experienced technical ...

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How much do machine learning physics postdoc jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for machine learning physics postdoc in the United States is $20.06, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $25.48 per hour, depending on experience, location, and employer.

What is a machine learning physics postdoc?

A Machine Learning Physics Postdoc is a researcher who applies advanced machine learning techniques to problems in physics. Their work often involves developing algorithms to analyze large datasets, simulate physical systems, or discover patterns within physical phenomena. They typically work in academic or research institutions, collaborating with physicists, computer scientists, and engineers. The role requires expertise in both physics and machine learning, as well as strong programming and analytical skills.

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

To thrive as a Machine Learning Physics Postdoc, you need a PhD in physics or a related field, strong mathematical and programming skills, and experience in applying machine learning to scientific problems. Proficiency with tools like Python, TensorFlow or PyTorch, and high-performance computing environments is typically required. Critical thinking, effective communication, and the ability to work collaboratively in interdisciplinary teams make candidates stand out. These skills are crucial for advancing research, developing innovative solutions, and translating complex physical phenomena into machine learning models.

What are common challenges faced by machine learning physics postdocs when integrating machine learning models with physical theories?

Machine Learning Physics Postdocs often encounter challenges in ensuring that machine learning models adhere to established physical laws and principles. Balancing predictive accuracy with physical interpretability can be demanding, as models may produce results that fit the data but violate known physics. Additionally, sourcing sufficient high-quality data for training and validating models, especially in specialized physics domains, can be a hurdle. Collaborating closely with both physicists and machine learning experts is essential to bridge gaps in domain knowledge and ensure robust, meaningful results.

What are popular job titles related to Machine Learning Physics Postdoc jobs?

For Machine Learning Physics Postdoc jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning Physics Postdoc job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $41,731 per year, or $20.1 per hour.

Physics Informed Machine Learning Scientist

San Diego, CA • On-site

ASML
Manufacturing • 10K+ employees
Learn more about ASMLexternal

Full-time

Re-posted 18 days ago

ASML rating

9.1

Company rating: 9.1 out of 10

Based on 42 frontline employees who took The Breakroom Quiz

23rd of 499 rated machine equipment manufacturers

Job description

Job Summary:
ASML is a leading company in developing lithography machines for the microchip industry. They are seeking a Physics Informed Machine Learning Scientist to join a research team focused on creating next-generation lithography light source technologies through advanced data management and machine learning methodologies.
Responsibilities:
• Establish a scalable data management framework spanning legacy and new datasets from test benches and source prototypes, ensuring data quality, accessibility, and structured readiness for seamless integration into ML workflows.
• Develop physics-informed machine learning models and scientific simulations to enable system-level tradeoff analysis and drive the definition and optimization of lithography source technology configurations.
• Adapt and integrate existing physics-based models into a master virtual model, and establish the necessary infrastructure for deployment and maintenance.
• Propose experimental anchoring studies, analyze test results, reduce model uncertainty through correlation building, and extract actionable knowledge from submodule- to full-system-level analysis.
• Provide input to technology roadmaps, identify de-risking activities and key scientific learning objectives, and contribute to experimental design to establish design guidelines, performance requirements, and procedures for product teams.
• Troubleshoot code and algorithms required for source operation, data streaming, storage, and queries.
• Document learnings and communicate knowledge to engineering and product development teams to guide product improvement and the release of new product nodes.
• Work independently and collaboratively to deliver on stated objectives, whether pursuing new knowledge, demonstrating new capabilities, or characterizing existing performance.
• Perform other duties as assigned or required.
Qualifications:
Required:
• Ph.D. with a minimum of 3+ years of experience or a Master’s degree with at least 6+ years of experience in an analytical field such as mathematics, physics, or engineering, with extensive experience in physics-informed machine learning and model integration into scalable master models.
• Experience solving complex, open-ended modeling problems using optimization and deep learning methodologies, with strong expertise in data management and building scalable data and training pipelines for end-to-end model development and training.
• Strong software development skills in Python, with experience in deep learning frameworks (e.g. PyTorch or JAX); proficiency in C/C++, and Matlab is a plus.
• Ability to clearly and logically communicate ideas and knowledge to various audiences.
• Demonstrated ability to work effectively as a part of a team and lead investigation and research efforts involving multiple stakeholders and constraints.
• Proven ability to build trust and credibility, enabling effective leadership through influence.
• The successful candidate will not only have excelled in their technical field, but will have demonstrated inter-personal and communications strengths.
• Deep understanding of scientific research methods and strong curiosity.
Preferred:
• Experience with database tools, automation frameworks, and experimental tracking platforms (e.g. MLflow) for managing end to end ML lifecycle.
• Experience working in cloud and development environments such as Azure Kubernetes Service (AKS), Google Distributed Cloud Edge (GDCE), Apache Spark, Azure Databricks, and related technologies.
Company:
ASML is a manufacturer of chip-making equipment. Founded in 1984, the company is headquartered in Veldhoven, NLD, with a team of 10001+ employees. The company is currently Late Stage.

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