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Field
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materials that combine memory and learning directly within their structure, uniting theory and experiment. These materials adapt their computations over time, reducing energy use and improving efficiency
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mathematical integration, with the desired outcome being to increase the water and/or nitrogen use efficiency of Australian cropping systems. We propose to use a range of emerging data science approaches within
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subgroups—in shaping how teams collaborate and perform under pressure. By advancing knowledge on these dynamics, we seek to contribute to both scientific theory and practical solutions for organizations. Your
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. By advancing knowledge on these dynamics, we seek to contribute to both scientific theory and practical solutions for organizations. Your colleagues: This PhD position is part of a larger project
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Degree Dr rer nat Course location Berlin In cooperation with Freie Universität Berlin, Institute of Mathematics Humboldt-Universität zu Berlin, Institute of Mathematics Teaching language English
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mathematical modelling? Then you could be the ideal PhD candidate for this position. Self-assembled structures of colloidal particles and/or polymers at a liquid-air interface can be spontaneously generated when
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to generate the necessary knowledge and tools to equip local decisionmakers to better deal with conflicting values in parallel transitions and requires an interest in both theory development and engagement with
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behavior of programs at a high level. Automata theory — to manipulate logical formulas and domain representations. Two-player games — to reason about strategies and synthesized programs. The work involves
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mathematics are welcome to apply. Hands-on experience in optical laboratories will be a plus. How to apply: Please visit the UCL i4health CDT webpage linked above for instructions on the application process. In
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Description of the offer : The condensed matter theory group at Institut Néel (CNRS, Grenoble) invites applications for a three-year PhD position in condensed matter physics starting from October