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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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breakthroughs by: Developing a dynamic optimisation framework for household demand response (DR) and demand flexibility. Exploring demand response options, including innovative contract designs and dynamic
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and manipulating the electronic structures of solid-state materials, to tackle the significant and diverse challenges facing society as we transition away from technologies reliant on fossil-fuels
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well organized, structured, self-driven, and enjoy interacting and collaborating with colleagues, including PhD students and postdocs. You are also expected to take part in the supervision of BSc and MSc
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Kontogianni. Our research explores how intelligent systems can perceive, understand, and interact with the 3D world. We develop new methods in computer vision, machine learning, and multimodal 3D
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on understanding the key processes that govern the structure and function of pelagic food webs, as well as climate and ecosystem interactions. The research in population ecology establishes how processes affecting
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-house facilities as well as synchrotron X-ray sources. By obtaining multi-scale (spatial resolution as well as time-resolution) information about the transformation dynamics in zinc-air electrodes and
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well as synchrotron X-ray sources. By obtaining multi-scale (spatial resolution as well as time-resolution) information about the transformation dynamics in zinc-air electrodes and follow quantitatively
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stimulating working conditions in a dynamic and international research environment. Our research facilities include modern laboratories and the state-of-the-art technologies. Terms of salary and employment
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to the cyclotron motion of electrons near the plasma center. However, recent models predict that as these waves traverse the plasma edge, they may be influenced by non-linear plasma–wave interactions. Some studies