25 postdoc-in-thermal-network-of-the-physical-building Fellowship positions at UNIVERSITY OF SOUTHAMPTON
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, MediaPipe, Blender) with radar signal models, generating synthetic radar datasets, and validating them with real-world measurements from radar hardware. As part of the team, you will: Build and validate radar
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. This project is part of the RIIO-2 Network Innovation Allowance (NIA), to work on innovative solutions to challenges the electrical transmission system faces. NIA projects enable the energy transition or support
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and Physical Sciences Research Council) project: 3D Polysilicon Photonics - A New Platform for Integrated Optoelectronics. The research will focus on the development of a flexible polysilicon platform
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The position will be held in the Centre for Cancer Immunology at Southampton General Hospital. This Centre builds on a 40-year history of pioneering immunology and cancer research at Southampton and
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cellular defects relating to cilia motility, prior to high throughput testing of novel therapies. Through the national LifeArc collaboration you will have opportunities to network and work with scientists
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://pubs.acs.org/doi/10.1021/acs.nanolett.1c04604 . Our project - sponsored by the UK’s Engineering and Physical Sciences Research Council – aims to improve the efficacy, efficiency and reproducibility of focused
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is inbodied interaction. Some of the projects you’d be engaged in, towards building your own questions: XB – directed self-experimentation for tuning personal knowledge skills and practice Experiment
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building sustainable partnerships across the education and health sectors. This post will be based at LifeLab at the University Hospital Southampton with hybrid working opportunities, and additional working
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Health or Social Science subjects and have: Excellent research skills with experience of building and maintaining relationships with clinical and research staff Experience of recruiting study participants
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drivers, and how do these intersect to create wet and dry extremes? How can improved representation of SM variability at process scales enhance monitoring and prediction, benefitting SM-dependent