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and motivated PhD student to join an interdisciplinary project that combines computational biology, spatial transcriptomics, and tumor modeling to understand how the aggressive brain tumor glioblastoma
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systems on different temporal and spatial scales. For our Research Group Applied Optimization we are looking for a PhD student: New Deep Learning - based Framework for Energy Modelling: Combination
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sites will not be located directly at the source of emissions, which will require transport infrastructure. As part of this work, you will model spatially resolved transformation paths for the energy
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clinical prediction of progression remains difficult, leading to over- and undertreatment of women with particularly early breast cancer. Over the past decade, spatial tissue analysis techniques have been
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the aim of taking the complex spatial structure of clouds into account in climate models and global remote sensing. The position thus offers highly relevant and interesting opportunities for work and
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social acceptability. D2ET will develop a comprehensive digital platform for planning energy transition scenarios, leveraging a consolidated data model and advanced analytics to facilitate strategic
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KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science Project description Third-cycle subject: Computer Science This project involves generative modeling
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or local scales due to spatial, temporal, and sectoral heterogeneities. Moreover, the availability and quality of energy-related models and datasets at these levels are often limited, further complicating
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for sufficiency in residential architecture Design and evaluate sufficiency measures for new and existing residential buildings Advance the knowledge on the value of spatial quality versus area and volume Develop a
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PhD Position on Unravelling the Social Exposome for Better Health Faculty: Faculty of Geosciences Department: Department of Human Geography and Spatial Planning Hours per week: 36 to 40