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industrial application Related works: [1] https://arxiv.org/pdf/2404.09932 (Relevante Abschnitte: 2.7, 3.5, 3.6) [2] http://arxiv.org/pdf/2403.00108 [3] http://arxiv.org/pdf/2411.17453 [4] http://arxiv.org
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search will be used to explore combinations. Results: The developed methods are intended to allow the training of balanced but also specialised computer vision models, particularly in the field of face
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optimization of technical processes and products. We present the exciting range of applications of our collaborative software development MESHFREE: https://www.meshfree.eu/ Currently, our team is working
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Related works: https://arxiv.org/pdf/2404.09932 (Relevante Abschnitte 2.2, 2.7, 3.1, 3.2) https://arxiv.org/pdf/2402.05162 https://arxiv.org/pdf/2406.14144 We value and promote the diversity of our
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experience with PyTorch. Ideally, knowledge in computer vision and object detection/segmentation. Motivation to independently delve into new and current research topics. Willingness to work with erotic
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skills, preferably some experience with PyTorch. Ideally, knowledge in computer vision and object detection/segmentation. Motivation to independently delve into new and current research topics. Willingness
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this, for example, through our onboarding program for new employees as well as by raising awareness of and providing training on diversity and equal opportunity issues. We firmly believe that diversity is key to our
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comfortable with us from the start. We achieve this, for example, through our onboarding program for new employees as well as by raising awareness of and providing training on diversity and equal opportunity
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(https://www.ise.fraunhofer.de/en/research-projects/pvev.html ), we are working to optimize models for PV self-consumption estimation and, on that basis, to develop an algorithm for PV feed-in upscaling
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, you will be supported by your colleagues in the team. What you contribute Are you studying Mechanical Engineering, Electrical Engineering, Energy Technology, Technical/Applied Computer Science, Data