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are invited for two fully funded PhD studentships in Computer Vision and Machine Learning on the topic of Detailed Video Understanding. The aim is to go beyond the coarse-grained labels that current models can
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quality of traditional HTR algorithms. We are looking for a highly motivated and qualified PhD student/researcher who will work towards developing new (machine learning and deep learning-based) algorithms
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Center (NPEC) . You will utilise modelling, genetic mapping, and machine learning techniques to identify genes controlling the growth during and after an immune response. In the second phase of the project
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new (machine learning and deep learning-based) algorithms and procedures that exploit human labelling and machine-based clustering efficiently. Human labelling (from Dutch cultural heritage partners
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friendly solution for mitigating global warming. Due to their high power density, permanent-magnet-based electrical machines are commonly used in electric vehicles. However, these machines have several
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main responsibility will be to work on research projects in generative machine learning. These projects require both training of deep neural networks on large datasets and performing theoretical analyses
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, scalability, interpretability, and explainability of AI. The UAI group is looking for a highly motivated and skilled PhD candidate to work in the area of probabilistic machine learning. The position is fully
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, preferably in Python. Experience with deep learning, machine learning, and image analysis or time series analysis is a plus. Lastly, you have good communication skills and you enjoy working in a
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); demonstrated interest in AI reasoning systems, knowledge representation, context-aware pervasive computing, machine learning and data analysis, software engineering; good programming skills; high motivation in
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), and machine learning techniques to identify new players of plant growth during and after an immune response. You will validate the genetic leads, such as transcription factors (TFs), identified in our