109 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"HFML-FELIX" PhD positions in Netherlands
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decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch energy
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Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
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learning, and new financing approaches. A preliminary research plan looks as follows. First, you will develop and apply quantitative approaches to analyse the present and future water consumption (i.e
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Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; a complete educational program for PhD students; multiple courses on topics such as leadership for academic staff
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and support well-founded decision-making within the programme. You design and organise engaging workshops and learning experiences for students, connecting digital and technological developments to real
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compositionally complex recycled steels, using density functional theory and machine-learned interatomic potentials, in close collaboration with leading academic partners and Tata Steel. Job description At TU Delft
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fMRI to translate the findings from the first part and develop a real-time fMRI neurofeedback paradigm through which participants learn to regulate the identified brain region. The ultimate aim is to
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-learning models that connect powder characteristics and process parameters with the properties of the final components. These models will support faster feedstock qualification and enable predictive quality
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-source scientific software library. You will be part of a larger project at the Uncertainty in Complex Systems lab (PI: Dr Max Hinne) aimed at learning which statistical and/or computational model is most
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at Saxion University of Applied Sciences, you will contribute to the development of machine-learning models that connect powder characteristics and process parameters with the properties of the final