Jr Specialist NEX in Mechanical Engineering

The Department of Mechanical Engineering at the University of California, Riverside is seeking a motivated Junior AI/ML Specialist to join our environmental research and data science team. Applicants must hold a Bachelor's degree in Computer Science, Data Science, Physics, Environmental Science, or a related quantitative field.

In this role, you will apply machine learning techniques to one of the most challenging problems in fluid dynamics: turbulence prediction within environmental datasets. You will work at the intersection of atmospheric science, optics, and physics, helping us translate complex sensor data and numerical simulations into actionable predictive models for climate and environmental monitoring.

Key Responsibilities:

* Data Pipeline Management: Process and clean large-scale environmental datasets (e.g., LiDAR, satellite imagery, and weather station arrays).

* Model Development: Assist in designing and training neural networks (CNNs, RNNs/LSTMs, or Physics-Informed Neural Networks) to predict turbulent flow and dispersion.

* Feature Engineering: Extract meaningful physical parameters from "noisy" environmental data to improve model accuracy.

* Validation & Testing: Compare ML model outputs against empirical field measurements.

* Collaboration: Work alongside senior faculty and graduate students to understand physical results and correlations and develop understanding into broader environmental forecasting systems.

* Course Development: Work alongside senior faculty and graduate students to develop coursework and course materials related to research outcomes and project efforts.

Technical Requirements:

* Programming: Proficiency in Python and standard ML libraries (PyTorch, TensorFlow, or AutoML).

* Math & Physics: A solid understanding of linear algebra, calculus, and ideally, basic fluid dynamics or atmospheric physics.

* Data Handling: Experience with high-dimensional data formats like NetCDF, HDF5, or GRIB and at least one satellite dataset

* Soft Skills: A "curious tinkerer" mindset-turbulence is chaotic, and finding patterns requires persistence and analytical rigor.

* Writing: Experience preparing figures for presentations, providing results for intermediate reports and preliminary data discussion.

* Data Visualization and Presentation: Excellent didactic skills in data visualization and presentation skills of quantitative data

Preferred Qualifications:

* Experience with academic writing (for example, for a journal publication and responding to comments/criticism)

* Background knowledge of turbulence and environmental measurements (MOST models, Cn2, anemometer, scintillation).

* Familiarity with translating models across different datasets, additive-feature-attribution for interpreting machine-learning models in fluid dynamics and heat-transfer systems.

To apply, candidates should submit a cover letter (including their research area(s) and specialization), a curriculum vitae (CV), and, optionally, up to three letters of reference. Applications must be submitted through UC Riverside Academic Personnel Recruit-Position JPF02248 Application Portal. Full consideration will be given to applications received by April 13, 2026, though the position will remain open until filled. The position is expected to start April 20, 2026. Selected applicants will be invited to interview via Zoom and provide a 15-minute presentation.

For more information about the Department of Mechanical Engineering.

The Jr. Specialist salary range $26.35 -$28.07 an hour. The posted UC salary scales set the minimum pay determined by experience level. UCOP Compensation Salary Scale. For additional information, UCNet RA Union Contract

* Applicants must hold a Bachelor's degree in Computer Science, Data Science, Physics, Environmental Science, or a related quantitative field. Have experience in the following areas:

Key Responsibilities:

* Data Pipeline Management: Process and clean large-scale environmental datasets (e.g., LiDAR, satellite imagery, and weather station arrays).

* Model Development: Assist in designing and training neural networks (CNNs, RNNs/LSTMs, or Physics-Informed Neural Networks) to predict turbulent flow and dispersion.

* Feature Engineering: Extract meaningful physical parameters from "noisy" environmental data to improve model accuracy.

* Validation & Testing: Compare ML model outputs against empirical field measurements.

* Collaboration: Work alongside senior faculty and graduate students to understand physical results and correlations and develop understanding into broader environmental forecasting systems.

* Course Development: Work alongside senior faculty and graduate students to develop coursework and course materials related to research outcomes and project efforts.

Technical Requirements

* Programming: Proficiency in Python and standard ML libraries (PyTorch, TensorFlow, or AutoML).

* Math & Physics: A solid understanding of linear algebra, calculus, and ideally, basic fluid dynamics or atmospheric physics.

* Data Handling: Experience with high-dimensional data formats like NetCDF, HDF5, or GRIB and at least one satellite dataset

* Soft Skills: A "curious tinkerer" mindset-turbulence is chaotic, and finding patterns requires persistence and analytical rigor.

* Writing: Experience preparing figures for presentations, providing results for intermediate reports and preliminary data discussion.

* Data Visualization and Presentation: Excellent didactic skills in data visualization and presentation skills of quantitative data

Preferred Qualifications

* Experience with academic writing (for example, for a journal publication and responding to comments/criticism)

* Background knowledge of turbulence and environmental measurements (MOST models, Cn2, anemometer, scintillation).

* Familiarity with translating models across different datasets, additive-feature-attribution for interpreting machine-learning models in fluid dynamics and heat-transfer systems.

The University of California, Riverside is a world-class research university with an exceptionally diverse undergraduate student body. UCR is a member institution of the American Association of Universities (AAU) as well as the Alliance of Hispanic Serving Research Universities (HSRU). A commitment to the UCR mission is a preferred qualification.

References are optional. If you are providing reference letters, please combine and upload all reference letters

and include them in the Letters of Reference.

The University of California, Riverside is a world-class research university with an exceptionally diverse undergraduate student body. UCR is a member institution of the American Association of Universities (AAU) and the Alliance of Hispanic Serving Research Universities (HSRU). A commitment to the UCR mission (https://apro.ucr.edu/mission-statement) is a preferred qualification.

We seek to hire scholars who will both advance our research directions and effectively educate our undergraduate and graduate students, while also engaging with University and Professional service activities. Research and teaching statements that are included with application materials are opportunities for candidates to share knowledge, experience, and goals that support the mission of UCR. For more information on UC's criteria for successful faculty, refer to the Academic Personnel Manual (APM) 210 - Criteria for Appointment, Promotion, and Appraisal (https://www.ucop.edu/academic-personnel-programs/_files/apm/apm-210.pdf).

The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration 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 or federal law. It is the policy of the University of California to undertake affirmative action and anti-discrimination efforts, consistent with its obligations as a Federal and State contractor.

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, State, or local government directives may impose additional requirements.

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.

"Misconduct" means any violation of the policies or laws governing conduct at the applicant's previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment or discrimination, as defined by the employer. A Misconduct Disclosure Survey will be completed through Truescreen, which is the vendor that administers this process for the campus.

For the University of California's Violence and Sexual Harassment Policy please visit:

https://policy.ucop.edu/doc/4000385/SVSH.

For the University of California's Anti-Discrimination Policy for Employees, Students, and Third Parties, please visit: https://policy.ucop.edu/doc/1001004/Anti-Discrimination.

For the University of California's Affirmative Action and Nondiscrimination in Employment Policy, please visit: https://www.ucop.edu/academic-personnel-programs/_files/apm/apm-035.pdf.