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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You
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and development to improve clinical processes for the benefit of our clinical partners and, in the end, patients. What you will do It has been observed that deep learning models are able to identify
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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(preferably in Python) Very good knowledge in German or English Please feel free to apply for the position even if you do not have all the required skills and knowledge. We may be able to teach you missing
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well as to leading HPS and STS centers in Germany and around the world. The researcher taking on this position will be required to teach 5 hours per week, in accordance with postdoctoral workloads across Germany. The
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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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the sections of Kl, Machine Learning and Deep Learning are your foundation. Are you proficient in relevant frameworks such as PyTorch, TensorFlow or Huggingface? Perfect! Additional experience in data (pre
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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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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