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                Job description The TU Delft’s Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS), invites applications for a fully funded PhD position. This exciting project aims 
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                values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft 
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                , Engineering, Art, and Mathematics) teaching and research. We foster an interdisciplinary environment where researchers from art, design, and STEM—particularly engineering—collaborate. Through design-led 
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                to future climate and human use scenarios; 3) Investigate the merits and drawbacks of DBN models in different geographic locations and over varying timescales; 4) Translate DBN model outputs into policy 
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                (mathematical modeling in ecology, LOMIC, Banyuls-sur-Mer) and Prof. Christoph Grunau (Host-Pathogen-Environment Interactions, IHPE, Perpignan). Both laboratories are equipped with all the necessary computational 
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                values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft 
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                values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft 
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                you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together 
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                and sensing. Macrocycles and capsules are classes of molecular hosts that are able to bind guest molecules or ions within their cavities(1). The confined microenvironment of these cavities can differ 
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                EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization ofquality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e. g. random forest (RF), artificial neural network (ANN)) will be applied using the parameters