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of microorganisms in fluids. Living systems such as bacteria or algae exhibit remarkable capabilities: they swim, adapt, interact, and self-organize into dynamic patterns. Understanding and replicating these life
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adaptation and learning from their experiences. Using a combination of theory, numerical experiments and precision desktop experiments, we will create 3D materials with self-adapting elastic elements
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resilience and enable early adaptation. Your Tasks As part of this PhD project, you will contribute to the development of innovative, interdisciplinary approaches to impact-based forecasting for vulnerable
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experiment. These materials adapt their computations over time, reducing energy use and improving efficiency. Partnering with Toyota and Demcon TSST, SMIP will showcase this innovation through a self-learning
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Work Activities We are seeking a motivated PhD- student to join our teams working on bistable
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capabilities: they swim, adapt, interact, and self-organize into dynamic patterns. Understanding and replicating these life-like dynamics in synthetic systems remains a major challenge across physics, chemistry
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which we live and work is changing rapidly and keeping up is anything but self-evident. At TSB, we want to understand how people function as individuals, in relationships, in organizations and in society
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and self-motivated PhD candidate to contribute to our understanding of advances, gaps and opportunities for impact-based forecasting for vulnerable population groups across Africa. You will work at the
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to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
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team, including another PhD student who focusses on the genomics of adaptation and a postdoc who will analyse the evolved strains using mass spectrometry, in collaboration with bioinformatician Dr