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Physics Informed Machine Learning Jobs in Colorado

... Machine Learning (ML) techniques. Experience in Reinforcement Learning (RL), Computer Vision, or ... physics, and/or mathematics. Experience with PyTorch, TensorFlow, or other deep learning frameworks ...

Senior Algorithm Engineer

Westminster, CO · On-site

$106K - $145K/yr

... machine learning and computer vision technologies. • Collaborate with software engineers ... Mathematics, Physics, Remote Sensing, Geospatial Sciences, or a related technical field. • ...

Data Scientist

Aurora, CO · On-site

$100 - $160/hr

... Machine Learning, Information Systems, Operations Research, Physics, Computational Biology, etc. ALTERNATE EXPERIENCEGeneral comment on degrees: Most contracts allow additional experience (4-5 years ...

Astrodynamics Engineer

Westminster, CO · On-site

$120K - $180K/yr

Review machine-learning model results against orbital mechanics and physical expectations. * Provide physics-based inputs to concepts of operations and systems-engineering activities. * Evaluate ...

Showing results 41-60

Physics Informed Machine Learning information

What is a physics informed machine learning?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the typical challenges faced by professionals working in physics informed machine learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What are the key skills and qualifications needed to thrive in the physics informed machine learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What cities in Colorado are hiring for Physics Informed Machine Learning jobs?

Cities in Colorado with the most Physics Informed Machine Learning job openings:

Infographic showing various Physics Informed Machine Learning job openings in Colorado as of August 2026, with employment types broken down into 6% Internship, 43% Full Time, 45% Part Time, and 6% Contract. Highlights an 100% In-person job distribution.

Data Scientist - multiple levels - CLEARANCE and POLYGRAPH REQUIRED

Constellation Technologies, Inc

Aurora, CO • On-site

Full-time

Re-posted 28 days ago


Job description

Job Summary:
Constellation Technologies, Inc is seeking a Data Scientist with multiple levels of experience. The role involves working with Big Data, dataflows, and applying Artificial Intelligence/Machine Learning techniques to analyze and derive insights from large datasets.
Responsibilities:
• Devise strategies for extracting meaning and value from large datasets.
• Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application specific knowledge.
• Through analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent to Agency data holdings.
• Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data.
• Effectively communicate complex technical information to non-technical audiences.
• Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting Agency collection, processing, storage and analytic capabilities and limitations.
Qualifications:
Required:
• Must be a US Citizen
• Must have TS/SCI clearance w/ active polygraph
• This position is open to multiple levels of years of experience; two (02) years within the last five (05) years must be directly related to the job you are applying for:
• Level 04 requires a minimum seventeen (17) years of experience w/ Degree
• Level 03 requires a minimum twelve (12) years of experience w/ Degree
• Level 02 requires a minimum five (05) years of experience w/ Degree
• Degree in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science. A degree in a related field (e.g., Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g., physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e., behavioral, social, and life) may be considered if it includes a concentration of coursework (typically 5 or more courses) in advanced mathematics (typically 300 level or higher; such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g., algorithms, programming, data structures, data mining, artificial intelligence). College-level Algebra or other math courses intended to meet a basic college level requirement, or upper-level math courses designated as elementary or basic do not count.
• Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python) and skill in at least one mid-level language (e.g. C)), data mining, advanced statistical analysis (e.g. statistical foundations of machine learning, statistical approaches to missing data, time series), advanced mathematical foundations (e.g. numerical methods, graph theory), artificial intelligence, workflow and reproducibility, data management and curation, data modeling and assessment (e.g. model selection, evaluation, and sensitivity.
• Employ some combination (2 or more) of the following areas: Foundations (Mathematical, Computational, Statistical); Data Processing (Data management and curation, data description and visualization, workflow, and reproducibility); Modeling, Inference, and Prediction (Data modeling and assessment, domain-specific considerations).
• Devise strategies for extracting meaning and value from large datasets.
• Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application specific knowledge.
• Through analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent to Agency data holdings.
• Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data.
• Effectively communicate complex technical information to non-technical audiences.
• Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting Agency collection, processing, storage and analytic capabilities and limitations.
Preferred:
• Fully Cleared polygraph is preferred
• Knowledge of working with Big Data, dataflows, Machine Learning/Artificial Intelligence familiarity.
• Analytics in GME, Jupyter notebooks, and Spark.
Company:
Constellation Technologies, Inc. Founded in 2008, the company is headquartered in Columbia, USA, with a team of 51-200 employees. The company is currently Growth Stage.