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want to develop machine learning approaches that not only withstand adverse conditions but actively learn from their own failures – and can you back those systems with rigorous formal guarantees? We
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perform both empirical and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in
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programmable biomineral composites. You will perform research tasks in Work Package 2: Material Formulation, Bio-Fabrication & Characterization. Your task will focus on designing porous mineral scaffolds and
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably
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years). The preferred starting date is the end of 2026. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings
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motivation for the subject Analyze data, assess different perspectives and draw well-founded conclusions Be flexible and open to adjusting the plan for the project as needed Collaborate and communicate
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of artificial intelligence and consumer behavior. • Designing, developing, and evaluating artificial intelligence (AI)-based techniques and algorithms related to consumer behavior and manipulative online
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countries (HIC). The airm of this PhD project is to understand how E. coli colonization contributes tot he risk of diarrheal disease and infections with AMR and use this knowledge to design interventions
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conducting empirical studies in higher education and coordinating data collection; examining teaching practices informed by Universal Design for Learning, the sensory environment and social inclusion
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Sylt Ost, Schleswig Holstein | Germany | 8 days ago
the opportunity to combine marine fieldwork, autonomous environmental monitoring, geochemistry and interdisciplinary data integration within one PhD project. Your Tasks Plan and participate in marine field