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. The project addresses the urgent need for higher education to adopt AI technologies that support rather than replace human-centred, transdisciplinary, and critical learning approaches. Using mixed methods
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programming skills (Fortran, Python or similar); affinity with spatiotemporal data analyses and numerical modelling; strong reporting and presentation skills; a good level of written and spoken English. Our
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couplings—a key technology for achieving passive, high-precision, and deterministic alignment between precision components. Unlike active alignment methods that rely on actuators and sensors, kinematic
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Application deadline: 15 May 2025 Apply now Are you fascinated by the complex ways our social environments shape health? Do you want to develop cutting-edge methods to measure social networks at a population
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analysis systems, to ensure safe and trustworthy results. This can involve research questions from NLP and AI like model robustness and guardrails, human-computer interaction such as interpretability and
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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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of real-world data, we urgently need to develop robust methods and governance frameworks that ensure the production of high-quality, reliable evidence for decision-making Job description We are looking
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-year PhD program, you will join a collaborative research team applying cutting-edge methods from Experimental/Behavioral Economics alongside modern macroeconomic modelling techniques (e.g. DSGE). You'll
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environmental and climate impact. Together with Dutch farmers, ReGeNL is a transdisciplinary program developing a future-proof agricultural sector in which agriculture goes hand in hand with soil health and
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(LC-MS), you will explore the occurrence, diversity, and transformation of PAs in food matrices. Your research will involve: developing sensitive and selective LC-MS methods using deuterated internal