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Computer Science or related fields • Strong background in machine learning • Strong programming skills in Python and experience with deep learning frameworks (PyTorch or similar) • Proficient in spoken and written
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such as ecology, economy and social sciences. ZMT aims to use data science tools, including computer vision and deep learning, for the study of rapid changes in tropical coastal socioecological systems
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) department develops innovative deep learning technologies in the area of image and video analysis. The department's competencies cover the entire processing chain, from the collection and analysis of visual
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foundations of ML · Explainability, trustworthiness, and robustness of ML · Deep learning architectures and Generative AI · Big data and cloud-based systems · Human-centered data analytics and data
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Experience in path planning / trajectory planning or traffic optimization is desirable Knowledge of programming languages such as Python and experience with deep learning frameworks (e. g. PyTorch) Passion
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or Mechanical Engineering – strong background in machine learning, deep learning and / or computer vision – good programming skills in Python (and C++) – basic knowledge of optics including concepts like PSF, MTF
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provide a range of services and applications, from standard workstations to artificial intelligence and deep learning. The digital transformation is a strategic guiding principle for the development
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existing AI models that use camera images to make statements about the current process status. You will develop a deep learning model that combines data from cameras and sensors to capture multispectral
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) Mathematical Modeling, Optimization, and Simulation Classical Image Processing and Machine/Deep Learning Probalistic Sensor Data Processing ( Kalman Filter, etc.) What you can expect A dynamic work environment
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-year bachelor’s degree in chemistry, chemical engineering or adjacent areas - High motivation and joy to learn about chemistry and chemical engineering - Strong commitment to solve some of the most