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    AI Research Scientist - Pulheim, Deutschland - Cubonic GmbH

    Cubonic GmbH
    Cubonic GmbH Pulheim, Deutschland

    vor 1 Tag

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    Position Overview:

    Cubonic GmbH is seeking a talented AI Research Scientist to join our dynamic team in Pulheim. The ideal candidate is passionate about leveraging AI and machine learning techniques to extract valuable insights from vehicle data and drive innovation in the automotive sector. This role presents an exciting opportunity to work on cutting-edge technology, collaborate with cross-functional teams, and contribute to the development of next-generation digital solutions.

    Key Responsibilities:
    • Conduct research and development in the field of artificial intelligence, with a focus on analyzing large datasets of vehicle telemetry, sensor data, and other relevant sources.
    • Develop and implement advanced machine learning algorithms and models to predict vehicle performance, maintenance needs, and potential failures.
    • Collaborate closely with engineers, data scientists, and other stakeholders to understand business objectives and translate them into AI-driven solutions.
    • Optimize autonomous driving algorithms and enhance vehicle performance through the application of machine learning techniques.
    • Explore new data sources, technologies, and methodologies to continuously improve data analysis capabilities and drive innovation.
    • Communicate findings, insights, and research outcomes effectively to cross-functional teams, management, and external stakeholders.
    • Contribute to the development of Cubonic's AI research roadmap and strategy, identifying opportunities for innovation and growth.

    Required Qualifications

    • Master's or Ph.D. degree in Computer Science, Electrical Engineering, Statistics, or a related field with a focus on artificial intelligence, machine learning, or data science.
    • Proven experience in AI research and development, with a strong track record of analyzing large datasets, building predictive models, and implementing machine learning algorithms.
    • Proficiency in programming languages such as Python or R, with experience in data manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy).
    • Solid understanding of machine learning techniques and libraries (e.g., scikit-learn, TensorFlow, PyTorch) and their applications in real-world scenarios.
    • Strong analytical and problem-solving skills, with the ability to think creatively and innovate in a fast-paced environment.
    • Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.
    Preferred Qualifications:
    • Experience working with vehicle telemetry data or IoT devices in the automotive industry.
    • Knowledge of cloud platforms such as AWS or Azure, and experience with distributed computing frameworks.
    • Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Tableau) and techniques for presenting complex data insights.
    • Understanding of automotive systems, vehicle dynamics, and related technologies.

    Working at Cubonic

    Cubonic GmbH is a leading innovator in the development of sustainable electric LCV (Light Commercial Vehicle) solutions. We specialize in providing automated to autonomous driving capabilities, seamless connectivity, reliability, and enhanced productivity for our customers. Our approach encompasses vehicle manufacturing, autonomous drive technology, and digital services, with a focus on revolutionizing the automotive industry. We are also pioneers in developing a digital twin platform that captures vehicle data, enabling the creation of digital replicas for real-time analysis and optimization.

    Your way to us

    Join us at Cubonic GmbH and be part of a dynamic team driving innovation in the automotive industry. Together, we will shape the future of fleet management and transportation.

    Interested? Then apply at

    About us

    We believe in solving today's and tomorrow's transportation challenges more sustainably, efficiently and effectively

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