Job Properties
  • Job Type
    Full-time Position
  • Category
    Media, Art & Design
  • Languages
    English
  • Experience Required
    Entry
  • Degree Required
    Bachelor
    • Province
      Nijmegen
    • Date Posted
      July 27,2022
    • Entrusting Package
    • JSS
    • VISA
    • MOCKINTERVIEW
    • IMG_6430
    • Career Consultation
    • COVERLETTER CHECK

    Internship: Use of Machine Learning in Product Engineering

    Do you have an affinity for automotive and would like to gain practical experience in a multinational environment?

    NXP’s high performance mixed signal technologies for Automotive applications create new ways to make cars cleaner, safer, more comfortable, and more fun. NXP’s Advanced Analog (AA) Business Unit’s mission is to provide a broad portfolio of differentiated analog, mixed-signal, wireless, and energy management solutions that enable our customers to realize compelling green, safe, connected, and secure products for the automotive market.

    Within the Product Engineering (PE) team of Automotive Ethernet Solutions , we are continuously working on improving the efficiency of our daily work and improving quality of products we make. There are opportunities to apply machine learning algorithms to analyze large datasets generated during high volume production. We are looking for a student to develop and implement these algorithms. You will be working with the experts who know chip design and production and will have a chance to impact the future way of working in a significant way.

    WHAT'S IN IT FOR YOU

    • The opportunity to improve your technical and soft skills by working closely together with experts in the field

    • Gaining experience in working with global R&D teams in a multinational organization

    • Bringing about the next generation of innovative technologies with your contribution

    • Being a full member of a working environment with an informal culture, a can-do mentality and future technical ladder career development opportunities

    • Possibility to become part of NXP’s Young Professional Talent Pool

    WHO ARE WE?

    The biggest challenge of producing and delivering cost-effective and high quality products to our customers is analyzing large sets of data coming out of production and then making sense of it. Product engineers spend a significant amount of time sorting through the data to find the most interesting bits and assess the changes required in our product development and production flow.

    YOUR PROFILE

    • You are a Bachelor or Master student in Electronics with a focus on Machine Learning

    • You have good understanding of large data sets and statistical analysis

    • You have knowledge in Python

    • You have the ability to work independently and results-oriented

    • You have good written and verbal English communication

    SOME NICE-TO-HAVE SKILLS

    • You have knowledge of R and/or C++

    • You have relevant scripting knowledge

    • SQL-databases,

    • Queuing and messaging systems like Kafka,

    • Workflow tools like Airflow

    DURATION

    The internship will be full time (40 hours per week) for a minimum of 6 months.

    Please note that in order to be considered for an internship/working student, you need to be registered as a student during the entire period.

    Creating Secure Connections and Infrastructure for a Smarter World

    NXP Semiconductors N.V. (NASDAQ: NXPI) makes products and environments safer, more sustainable, and more secure with innovative connectivity and edge processing solutions for a smarter world.

    We are in the business of better. Not just better technologies, but better innovations to improve society. As the world leader in secure connectivity and processing solutions for embedded applications, NXP is solving the world’s most complex technology challenges to accelerate business innovation, enhance how we work, and advance how we live.

    Ready to create a smarter world? Visit our career website and follow us on social: LinkedIn , Facebook , Twitter .

     
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