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(7T fMRI, MR Spectroscopy), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics
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or (iv) develop new representations of phytoplankton physiology/productivity. The post will be based around using and/or developing the ocean model PISCES, which is a complex model representing the cycling
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: Otaniemi Campus, Espoo, Finland Join a cutting-edge research environment where architecture, landscape architecture, and artificial intelligence meet to address complex environmental challenges. About the
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to the differentiation of human iPSC into different cell lineages, and the assembly of complex tissue culture models from these cellular elements. The cells used in this experimental system will be genetically modified
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organisations, as the basis for co-created outcomes • Join external networks to share information, ideas and project news The Person Knowledge, Skills and Experience • Knowledge of English music history
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while also offering scalability to dynamic and complex environments. The PDRA will also lead in-field testing and deployments. Therefore, we require someone with excellent implementation skills and
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this, the Fellow will implement a universal design methodology for such fluids of complex rheology, using a Machine Learning (ML) algorithm to be incorporated in a Computational Fluid Dynamics framework. Training
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analysis techniques, including space syntax, isovist measures, and visual complexity assessments. The successful candidate will work closely with researchers at Cambridge and ETH Zurich to quantify spatial
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, green business, decision support. Understanding of the net zero and/or circular economy. Research Excellence Proven strong track record of high-quality publications. Ability to work independently and
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The post-holder will join a team of investigators working on the NERC-funded Pushing the Frontiers grant ‘Influence of complex source and environmental source conditions on eruptive plume height