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Social and Everyday explainability; application of Machine Learning (such as Reinforcement Learning), Symbolic AI techniques (such as formal systems), or NLP techniques, in Human-AI collaboration; Human
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machine learning, with the goal of revealing how genome architecture shapes physiology, ecology, and evolution. Unlock hidden biological patterns while developing key computational and academic skills. Your
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and the learning performance, or using causal representation learning for developing more effective and interpretable MARL with communication algorithms. As a PhD candidate, you will primarily perform
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation
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programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas: programming
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five other PhD candidates, where you regularly engage in knowledge exchanges to strengthen cross-disciplinary collaboration. Your work aims to advance risk classification beyond binary labels by learning
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas