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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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and population genetic research Deep understanding of Evolutionary Biology Experience or interest in learning lab work (e.g. DNA extractions and library preparations) Research experience with genomic
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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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weekly working time of 40 hours per week. The position can be filled on a part-time basis. Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use
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bodies down to the bottom of the deep sea. The Aquatic Life Foundation Project (AqQua ) will, for the first time, combine billions of images acquired with a variety of devices across the globe for large
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field such as computer science, bioinformatics, mathematics, computational life sciences, or related. Profound knowledge in machine learning, preferably deep learning for image data. Experience in
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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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environment (e.g. cleanroom, laboratory Deep knowledge of solid state physics and/or quantum information Experience with microfabrication and/or operating and calibrating quantum systems Experience with
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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