20 postdoc-in-thermal-network-of-the-physical-building Fellowship positions at UNIVERSITY OF SOUTHAMPTON
Sort by
Refine Your Search
-
. 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
-
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
-
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
-
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
-
://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
-
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
-
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
-
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
-
programming. Experience with sustainable design principles is a plus. This is a unique opportunity to work on pioneering advancements in sustainable aerospace alongside industry experts. If you’re ready to make
-
Science in the University of Southampton. ActivATOR will develop novel machine learning models that enable robots to leverage the motion of their own bodies (‘egomotion’) to make sense of acoustic environments