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Field
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and disease risk factors Investigate new approaches in the analysis of large neurophysiological datasets We are seeking a highly motivated candidate with: A strong master’s degree in psychology
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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-impact questions in environmental economics and labour economics. Key responsibilities for this role includes: Data collection, cleaning, and merging from very large-scale microdata sources (e.g., terabyte
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the effectiveness of anticipatory cash transfers in response to climate disasters, using large-scale RCTs in Bangladesh and other countries. It investigates how early interventions can mitigate the impacts of extreme
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
investigate the large area production of graphene, BN, MoS2 and other layered materials, optimize their transfer process in view of their application in energy, electronics and photonics. This will include
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experience should cover basic chemistry analytical skills to large scale pilot experience, hands-on, and data analysis. This includes experience on corrosion, kinetics, emission impurities, and solvent
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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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, interdisciplinary research. Our department remains at the forefront of engineering innovation, delivering sustainable solutions with global impact. As a large, multi-specialisation department, we thrive
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expertise and visionary thinking, with responsibilities including securing major funding, influencing research policy, and driving innovation. You will lead large, interdisciplinary teams, ensuring
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution