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Master's Thesis- Machine Learning Models for Data Preparation in Automotive CFD Simulation Process

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Job

  • Level
    Junior
  • Job Field
    Software, Data
  • Employment Type
    Full Time
  • Contract Type
    Internship / school internship
  • Salary
    from 700 € Gross/Month
  • Location
    Graz
  • Working Model
    Onsite
  • Job Summary

    In this master's thesis, you will develop a machine learning model to automate data preparation for vehicle CFD simulations, including creating training datasets and optimizing the model.

    Job Technologies

    Your role in the team

    • The aim of this master's thesis is to speed up the data preparation process for complete vehicle CFD simulation.
    • Data preparation is the bottleneck in current industrial CFD processes.
    • Preparing a complete vehicle geometry for external or internal flow investigation keeps an engineer busy for several days.
    • To speed up data preparation, a machine learning model should be developed to automate some portions of this process (e.g., mesh cleaning, part classification).
    • You will work together with CFD engineers as well as our machine learning experts and project engineers.
    • Conduct a literature review of previous works and define a schematic procedure.
    • Prepare a training database in ANSA.
    • Entweder aus historischen oder öffentlichen Daten.
    • Research of various neural network architectures which can be considered for this application (e.g., 3D point- and mesh-based models).
    • Create a first model and train it with the previously created dataset.
    • Optimize model setup if necessary.
    • Create documentation and propose additional work to speed-up data preparation.

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    Our expectations of you

    Education

    • Ongoing technical study at the university / university of applied sciences in mechanical engineering, automotive engineering, computer science or similar.

    Qualifications

    • A good understanding of Python coding and machine learning libraries (e.g., PyTorch, NumPy, TensorFlow) is essential.
    • Basics in Beta CAE Systems software package ANSA is advantageous but not necessary.
    • Strong interest in bringing state-of-the-art machine learning to real world industry applications.
    • High level of helpfulness and excellent communication skills.

    Experience

    • Basic knowledge and experience in the field of numerics and CFD are beneficial but not mandatory.

    This text has been machine translated. Show original

    Benefits

    Work-Life-Integration

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    Job Locations

    Map of company locations
    • Location Graz

      Steiermark

      Austria

    Topics that you deal with on the job

    This is your employer

    Magna

    Magna

    Graz, Klagenfurt, Lannach, Weiz, Weikersdorf Am Steinfelde, Traiskirchen, Sankt Valentin, Wien, München, Mühlhausen Im Täle, Sindelfingen, Ingolstadt, Leonberg, Sailauf, Schleiz, Köln, Heilbad Heiligenstadt, Wolfsburg

    Magna is a leading technology and mobility company, with 344 production plants and 93 product development, engineering, and sales centers in 27 countries. As the largest automotive supplier in the world, Magna is helping to shape the biggest transformation in auto history by leading the way in electric vehicles, autonomous driving, and innovative manufacturing processes. We offer you all of the opportunity to help create the next generation of cars – join us as we shape the future of mobility together!

    Description

  • Company Size
    250+ Employees
  • Founding year
    1957
  • Language
    German, English
  • Company Type
    Established Company
  • Working Model
    Hybrid, Onsite
  • Industry
    Vehicle Manufacturing, Supplier, Electronics, Automatization, Internet, IT, Telecommunication
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    Total

    (3 Reviews)
    4.0
    • Culture

      4.3
    • Workingconditions

      4.2
    • Engineering

      3.5
    • Career Growth

      4.1
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    Logo Magna

    Master's Thesis- Machine Learning Models for Data Preparation in Automotive CFD Simulation Process

    Salary
    from 700 € Gross/Month
    Location
    Graz
    Working Model
    Onsite
    Diversity
    Open for all genders
    English Only
    English only required

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