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Field
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adaptation research, or urban resilience is considered an advantage, but is not required. Knowledge of Mandarin Chinese is considered an asset, but is not mandatory. What we offer We offer a full-time (1.0 FTE
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a safe and inclusive environment in which everyone can flourish and contribute. Knowledge security screening can be part of the selection procedures of academic staff. We do this, among other things
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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are educated in a stimulating learning environment. Upon graduation, they have acquired the knowledge, insight and skills to make important and inspiring contributions to an increasingly international society in
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to develop (i) new theoretical insights on sheltered digital work as a ‘nascent occupation’ as well as (ii) actionable knowledge that supports social enterprises to organize and manage novel forms of sheltered
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with an excellent knowledge of the Middle Dutch and Middle Low German language, literature and culture. Knowledge of High German and Middle French will be considered an advantage. You are a communicative
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complexity of the project prior knowledge in Materials Science is required. Ideally, you have a proven background in thermophysical and x-ray diffraction measurement methods. We encourage candidates with
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relevant to molecular and/or materials discovery, such as DFT, MD, and ML–based property prediction. Basic knowledge of physical chemistry, thermodynamics, or electrochemistry. Proficiency in Python and
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workflows, turning geodata into new answer maps. Knowledge graphs can be used to model these transformations and to link geodata sources to questions. In this project we will apply symbolic and sub-symbolic
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, including cultural, religious or ethnic background, gender, sexual orientation, disability or age. We strive to create a safe and inclusive environment in which everyone can flourish and contribute. Knowledge