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systematic perturbation systems for understanding brain network Position Description: We are seeking a highly motivated postdoctoral scholar to join our interdisciplinary research team. The successful
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. The appointment is for one year, renewable for a second year given the availability of funds. The focus is on fundamental properties of simulated neural networks, like neural scaling laws, as they manifest in
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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and
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Nurses Network. Working closely with Dr Marion Waite and her team, you will support the recruitment of participants, as well as multi-site and multi-method data collection in person at specific NHS sites
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collaborators a research climate encouraging lively, open and critical discussion within and across different fields of research a work environment with close working relationships, networking and social
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lively, open and critical discussion within and across different fields of research a work environment with close working relationships, networking and social activities a workplace characterised by
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Nurses Network. Working closely with Dr Marion Waite and her team, you will support the recruitment of participants, as well as multi-site and multi-method data collection in person at specific NHS sites
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position of 1.0 FTE for 30 months or 0.8 FTE for 37 months; access to computational resources (HPC), GIS/data infrastructure, and datasets via collaborative networks; a supportive, interdisciplinary research
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disseminating results through presenting at conferences and writing manuscripts. Additionally, our lab works with an extensive network of world-class collaborators to study other aspects of GPCR signaling
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view