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                solar (thermal and PV) and geothermal energy with soil-based thermal storage to enable reliable 24/7 operation of adsorption desalination. We also develop low-cost sorption composites from locally 
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                R Holland, a preclinical research group with expertise in primary headache disorders, electrophysiology, disease modelling, behaviour, chemogenetics and in vivo neural imaging technologies 
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                About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with 
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                of neural activity will be used in conjunction with advanced imaging methods and behavioural assays to investigate how signals beginning in the retina and transmitted through different visual pathways 
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                experiments may involve images, sounds or real objects that can be seen and felt. The MPAC lab has a research dedicated laboratory equipped with state-of art facilities for behavioural testing, including eye 
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                access to data from AIMS-2-TRIALS, the world’s largest multimodal, longitudinal autism study; the South African BONO cohort, and the SLAM Image Bank, one of the world’s largest, real-world imaging/clinical 
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                , disease modelling, behaviour, chemogenetics and in vivo neural imaging technologies. The Headache Group is part of the WSPaRC, with broad expertise in sensory biology (pain, vision & hearing) and 
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                -up (Bruker dimension icon), and has further access at QMUL to SEM and TEM. We offer competitive salaries, access to a generous pension scheme, 30 days’ leave per annum (pro-rata for part-time/fixed-term), a 
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                Project The Adaptive Design for AI-Driven Processes in Transforming Dynamic Landscapes (ADAPT) project develops scalable, data-driven design methodologies that transform dynamic environmental processes 
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                to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short