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    Thesis: Bridging the Sim2Real gap for sensor data in an industrial context via generative models - Waldkirch, Deutschland - SICK AG

    SICK AG
    SICK AG Waldkirch, Deutschland

    vor 1 Monat

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    Beschreibung

    SICK is one of the world's leading solutions providers for sensor-based applications in the industrial sector. Founded in 6 by Dr.-Ing. e. h. Erwin Sick, the company with headquarters in Waldkirch, Breisgau near Freiburg ranks among the technological market leaders. With more than subsidiaries and equity investments as well as numerous agencies, SICK maintains a presence around the globe. SICK has more than , employees worldwide and generated a group revenue of around EUR 2.3 billion in the 3 fiscal year.

    SICK is one of the world's leading solutions providers for sensor-based applications in the industrial sector. Founded in 6 by Dr.-Ing. e. h. Erwin Sick, the company with headquarters in Waldkirch, Breisgau near Freiburg ranks among the technological market leaders. With more than subsidiaries and equity investments as well as numerous agencies, SICK maintains a presence around the globe. SICK has more than , employees worldwide and generated a group revenue of around EUR 2.3 billion in the 3 fiscal year.

    Thesis: Bridging the Sim2Real gap for sensor data in an industrial context via generative models*

    Thesis: Bridging the Sim2Real gap for sensor data in an industrial context via generative models*

    SICK AG SICK AG

    Waldkirch (bei Freiburg), DE,

    Waldkirch (bei Freiburg), DE,

    Full-time Full-time

    Winter semester 4/ - limited to 5-6 months

    Machine learning (ML) models are increasingly hungry for data. In industrial contexts, high-quality labeled data is a scarce and costly resource. Synthetic data enables ML model training, algorithm evaluation, or sensor design without the burden of exhaustive data collection. However, generating realistic data is difficult, because not all attributes of sensors or the environment are captured (i.e., simulation-reality-gap). Recently, generative models have emerged as a promising avenue, potentially helping in generating synthetic data that closely mimics real-world scenarios.
    The goal in this thesis will be to evaluate the potential of generative models to bridge the gap between realistic and synthetic sensor data in an industrial context.

    YOUR TASKS:

  • You perform a literature review on generating realistic sensor data via generative models
  • You familiarize yourself with sensor simulation in Nvidia Omniverse
  • You explore the applicability of 1-2 generative approaches on a specific industrial use case
  • You evaluate the performance of considered approaches on a downstream task

    • YOUR PROFILE:

  • You are familiar with generative models such as diffusion models or GANs
  • You may bring experience in machine learning domains such as computer vision, audio processing, or NLP, demonstrating expertise in system training, evaluation, and understanding of these systems
  • You might have designed your own games or tinkered with the industrial Metaverse
  • You are passionate about exploring the applicability of Generative AI for a specific industrial use case

    • YOUR APPLICATION:

  • We are looking forward to your online application
  • Sarah Disch
  • Job-ID
  • All applications will be treated confidentially

    • *At SICK, we see people, not gender.

    We put great emphasis on diversity, reject discrimination and do not think in categories such as gender, ethnicity, religion, disability, age or sexual identity.

    Stichworte: Student, Studentin, Studierende, Masterstudent, Bachelorstudent, Masterand, Bachelorand, Masterstudentin, Bachelorstudentin, Masterandin, Bachelorandin, Master, Bachelor, studienbegleitend, Studi, Studium, Bachelorstudierende, Masterstudierende, Praktikant, Praktikantin, Pflichtpraktikant, Pflichtpraktikantin, Praktika, Praktikum, Pflichtpraktikum, Internship, Intern, Praxissemester, Pflicht, Prakti, freiwillig, Praxis, Semesterpraktikum, Thesis, Masterarbeit, Bachelorarbeit

    Winter semester 4/ - limited to 5-6 months

    Machine learning (ML) models are increasingly hungry for data. In industrial contexts, high-quality labeled data is a scarce and costly resource. Synthetic data enables ML model training, algorithm evaluation, or sensor design without the burden of exhaustive data collection. However, generating realistic data is difficult, because not all attributes of sensors or the environment are captured (i.e., simulation-reality-gap). Recently, generative models have emerged as a promising avenue, potentially helping in generating synthetic data that closely mimics real-world scenarios.
    The goal in this thesis will be to evaluate the potential of generative models to bridge the gap between realistic and synthetic sensor data in an industrial context.

    YOUR TASKS:

  • You perform a literature review on generating realistic sensor data via generative models
  • You familiarize yourself with sensor simulation in Nvidia Omniverse
  • You explore the applicability of 1-2 generative approaches on a specific industrial use case
  • You evaluate the performance of considered approaches on a downstream task

    • YOUR PROFILE:

  • You are familiar with generative models such as diffusion models or GANs
  • You may bring experience in machine learning domains such as computer vision, audio processing, or NLP, demonstrating expertise in system training, evaluation, and understanding of these systems
  • You might have designed your own games or tinkered with the industrial Metaverse
  • You are passionate about exploring the applicability of Generative AI for a specific industrial use case

    • YOUR APPLICATION:

  • We are looking forward to your online application
  • Sarah Disch
  • Job-ID
  • All applications will be treated confidentially

    • *At SICK, we see people, not gender.

    We put great emphasis on diversity, reject discrimination and do not think in categories such as gender, ethnicity, religion, disability, age or sexual identity.

    Stichworte: Student, Studentin, Studierende, Masterstudent, Bachelorstudent, Masterand, Bachelorand, Masterstudentin, Bachelorstudentin, Masterandin, Bachelorandin, Master, Bachelor, studienbegleitend, Studi, Studium, Bachelorstudierende, Masterstudierende, Praktikant, Praktikantin, Pflichtpraktikant, Pflichtpraktikantin, Praktika, Praktikum, Pflichtpraktikum, Internship, Intern, Praxissemester, Pflicht, Prakti, freiwillig, Praxis, Semesterpraktikum, Thesis, Masterarbeit, Bachelorarbeit

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