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level Basic knowledge in at least one of the following areas: computer graphics, computer vision, or machine learning / neural networks Programming experience in Python; knowledge of PyTorch is a plus
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(Computer Science), in the research group “Computer Graphics and Virtual Reality” of Prof. Gabriel Zachmann, within the EU project SENSORAMA (Horizon Europe), we are seeking to fill the following position
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Future. Discover. Together. The Computer Vision & Graphics group of the Vision & Imaging Technologies (VIT) department is looking for a student assistant in the area of deep learning for scene
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cardiovascular disease. A possible direction for the work is the development of probabilistic graphical models that represent proteins as latent causal drivers of disease, while treating other omics layers as
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and electrical and optical properties, Evaluation and graphical presentation of process data and material properties. What you contribute The position is targeted at students in Physics, Materials
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and compare scenarios for renewable energy supply, ranging from heat pumps and geothermal energy to seasonal heat storage systems. You will support the creation of graphics and presentations. You will
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Max Planck Institute for the History of Science, Berlin | Berlin, Berlin | Germany | about 1 month ago
on scientific texts (mainly in English, German, and Chinese) developing graphic designs for the visualization of scientific results assistance in creating and managing the group’s institutional and additional
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in internationally visible media are expected. A focus in the area of digital editions as well as skills in research data management, current methods in optical character recognition and AI
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implementation of the touch interface, and expansion of the graphical user interface (GUI) to include new functions and design elements Topic 2: Development of a cable tester to test various cable types according
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in optical character recognition and AI are desirable. An integral component of the position’s profile are competences in the field of developing methods and infrastructure solutions for machine-based