About this role
Summary: Develop cross-disciplinary research methods combining heterogeneous evidence across scales to make predictions in sparse or unseen physical contexts. Requires expertise in multi-modal learning, physics-informed ML, and transfer learning for scientific systems.
Posting Title
Postdoctoral Researcher in Scientific Machine Learning and Cross-scale Predictive Modeling
.
Location
CO - Golden
.
Position Type
Postdoc (Fixed Term)
.
Hours Per Week
40
.
Working at NLR
NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.
Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.
At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.
Job Description
The Artificial Learning and Intelligent Systems group in NLR’s Computational Science Center has an immediate opening for a Postdoctoral Researcher to pursue a cross-disciplinary research project in cross-scale learning from heterogeneous evidence (lab-scale to pilot-scale) relevant to the DOE Office of Critical Minerals and Energy Innovation..
The successful applicant will develop methods that combine evidence across different experimental scales, conditions, and modalities to make predictions at unseen physical or operational scales where data may be sparse or unavailable. In this context, the relationships between inputs and outputs may change with scale, and the available data may come from different data sources with different modalities and amounts and have different physical contexts. We are looking for someone who has developed innovative learning methods, including multi-modal and surrogate modeling approaches, transfer learning techniques, physics-informed ML, and/or statistical learning methods.
.
Basic Qualifications
Must be a recent PhD graduate within the last three years.
\* Must meet educational requirements prior to employment start date.
Additional Required Qualifications
- Demonstrated experience developing ML models for scientific or engineering systems
- Demonstrated experience in learning from one set of conditions or domains and predicting in another, including situations where the source and target distributions differ.
- Demonstrated experience in combining data with different modalities, sampling rates, resolutions, fidelities, measurement characteristics, costs, accuracies, or experimental conditions.
- Demonstrated experience in physics-informed and hybrid modeling, showing the ability to incorporate mechanistic models, physical constraints, or known relationships when useful, without assuming that complete physics models are available.
- Demonstrated experience in model representation and adaptation, how to represent changes in system context, operating conditions, geometry, or scale so that the model can distinguish what is transferable from what is not.
- Excellent communication skills
- Demonstrated experience working in multi-disciplinary teams and collaborative software development
- Demonstrated experience with python and version control
Preferred Qualifications
.
Job Application Submission Window
The anticipated closing window for application submission is up to 30 days and may be extended as needed.
Annual Salary Range (based on full-time 40 hours per week)
Job Profile: Postdoctoral Researcher / Annual Salary Range: $76,600 - $126,400
NLR takes into consideration a candidate’s education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee’s salary history will not be used in compensation decisions.
Benefits Summary
Benefits include medical, dental, and vision insurance; short-term disability insurance\*; pension benefits\*; 403(b) Employee Savings Plan with employer match\*; life and accidental death and dismemberment (AD&D) insurance; personal time off (PTO) and sick leave; and paid holidays. NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement.
\* Based on eligibility rules
Badging Requirement
NLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation.
Drug Free Workplace
NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.
If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.
Submission Guidelines
Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
[Reasonable Accommodations](https://www.nlr.gov/careers/employment-policies)
E-Verify [www.dhs.gov/E-Verify](http://www.dhs.gov/E-Verify) For information about right to work, click [here](http://www.justice.gov/sites/default/files/crt/legacy/2013/08/13/FinalOSCPosterEN08_01_2013.pdf) for English or [here](http://www.justice.gov/crt/file/813271/download) for Spanish.
E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.