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found at https://accedtp.ac.uk/, in the ‘prospective applicants’ tab. Project overview Are you interested in a PhD working on the biological impacts of climate change, based in world-leading research
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 1 month ago
the A20 mouse lymphoma syngeneic model with anti-mouse PSGL-1. The lymphoma microenvironment spatial composition and immune cell activation states will be characterized by advanced techniques. We also plan
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Professor who wants to contribute to advance science, teaching and practice at the interface of Biodiversity, Land Use and Spatial Planning. By planning and governance of land use it is possible to halt
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation
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household and firm surveys (carried out by another team member) and other spatial and physical climate risk data. The goal of this agent-based modeling is to identify conditions under which risk contagion
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challenging conditions (see https://tissueresilience.com ). Harnessing powerful in vivo models, our work spans multiple biological scales - linking the molecular cell biology of individual cells to the health
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, or glaciology. We invite applicants to highlight experience with spatial analysis tools, such as GIS, or other quantitative approaches, which could include modeling or AI. The teaching load is three courses per
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contribute to projects using cutting-edge molecular, cellular, and spatial profiling approaches to dissect vascular and immune signaling across injury models. Key technologies and methodologies include
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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the influence of the engine plume on the release of emissions into different atmospheric layers. Numerical model to describe the temporal and spatial dispersion behaviour of engine exhaust gases in the atmosphere