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degree, preferably with a doctorate Previous knowledge in the fields of data science, machine learning and artificial intelligence, in particular a deep understanding of machine learning Very good
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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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cutting-edge methods in multi-omics data integration and the project will provide opportunities to learn, develop, and apply machine learning and deep learning methods on genomics data. Requirements
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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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Your Job: In this position, you will be an active part of our Simulation and Data Lab for Applied Machine Learning. Within national and European projects, you will drive the development of cutting
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to develop and implement an innovative training program for doctoral candidates. This extensive, in-depth training from expert researchers will enable the candidates to gain a deep understanding of the field
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to develop and implement an innovative training program for doctoral candidates. This extensive, in-depth training from expert researchers will enable the candidates to gain a deep understanding of the field
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role in the global carbon cycle by transferring carbon from the surface ocean to the deep sea through the formation, sinking, and remineralization of organic particles. Despite its importance, our
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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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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