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Internship Bosch Machine Operator Jobs in Illinois

Powered by LOIS , the Legal Operating Intelligence System, Filevine connects context across every ... machine learning systems extract from published legal materials. This role is ideal for law ...

Powered by LOIS , the Legal Operating Intelligence System, Filevine connects context across every ... machine learning systems extract from published legal materials. This role is ideal for law ...

Powered by LOIS , the Legal Operating Intelligence System, Filevine connects context across every ... machine learning systems extract from published legal materials. This role is ideal for law ...

The successful candidate will gain hands-on experience applying modern machine learning and ... To be considered for the internship, candidates must have permanent US Work authorization.

The successful candidate will gain hands-on experience applying modern machine learning and ... To be considered for the internship, candidates must have permanent US Work authorization.

$25 - $40/hr

Role Summary Internship Term: Fall 2026 Rivian internships are experiences optimized for student ... Implement hardware-aware optimizations, memory-efficient representations, and operator fusion ...

New

Validation Engineer

Belvidere, IL · On-site

$73K - $95K/yr

... Operator-Tech Manual & PowerPoint presentations, Machine Task Instructions, RIT BOM, spare parts ... coursework, internships and work experience in manufacturing * Minimum of 3 years' experience ...

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Internship Bosch Machine Operator information

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IMSA-SIR Developing Reduced-order Models for Predicting Aerodynamic Performance of Novel Wing Shapes

Argonne National Laboratory

Lemont, IL • On-site

Part-time

Posted 10 days ago


Job description

Internship Description
Background:
Computational fluid dynamics (CFD) is a widely utilized tool in Aerospace Engineering that can provide detailed information regarding aerodynamic performance; however, it is too costly to implement in real-time performance assessment. The use of data-driven surrogate models, which can be trained from CFD and experimental datasets, can be utilized for generation predictions for changes in the system. Additionally, these tools can be utilized for extracting complex dynamics into simpler representations which enable greater understanding and utilization. CFD simulations for various airfoil profiles have been performed and data characterizing their aerodynamic performance has been extracted. This dataset is available for developing surrogate models using machine learning and dynamical systems tools to provide predictions for off-design conditions, as well as inform potential geometries for optimal performance.
Description of Student Internship:
The flow over 2D airfoil profiles has been setup in the open-source software OpenFOAM. We will provide the structure for running simulations and post-processing the results using tools such as ParaView, as well as in-house python scripts. There is a small database of aerodynamic coefficients that has been collated for these airfoils for various operating conditions which is available for training data-driven surrogate models. The structure of the input and desired outcome of these models will be provided to the student, with the opportunity to explore various ML and related models for predictions of aerodynamic performance.
Education and Experience Requirements
Required skills:
Desired skills are a fundamental understanding of classical mechanics
Experience with aerodynamics is a plus
Interest in computing in Linux/Unix environments
Programming or interest to learn Python, Matlab
Interest in utilizing machine learning for engineering projects
Internship Family
Visiting Student High School Research
Internship Category
IMSA Student