Sort by
Refine Your Search
-
Listed
-
Field
-
mathematical ecosystem modelling with experimental data. Our lab strongly supports cross-disciplinary approaches to research. We straddle the clusters of Molecular Microbiology and Ecology and Evolution, and you
-
. Desirable Application / Interview Ability to work within a University research group and in collaboration with external partners. Desirable Application / Interview Further Information Grade Offscale Salary
-
consequently, resistance to standard drugs such as cisplatin and radiotherapy. This project will 1. Assess levels of this protein in biobanked bladder cancer tissue samples and correlate with stored patient data
-
quickly, in an automatic way huge amounts of video data. Different intelligent video surveillance systems have been developed in a wide range of applications. In most cases these are multiple camera systems
-
information: https://www.sheffield.ac.uk/postgraduate/phd/apply/english-language. Please see this link for information on how to apply: https://www.sheffield.ac.uk/cbe/postgraduate/phd/how-apply. Please include
-
and our preliminary data indicate that its subcellular localisation is altered when cells are grown on stiffer substrates. This is likely to have implications for proliferation of cancer cells within
-
, including computer vision and machine vision. As a project engineer, you will ensure successful project delivery, delivering continuous improvements to IMG processes and build AMRC’s reputation in computer
-
, experimental design and data analysis methods. In this project, you will use the following techniques: Cutting edge microscopy (confocal and FRET) to image biosensors to detect hormone and sugar concentrations
-
Advanced brain-computer interface School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Mahnaz Arvaneh Application Deadline: Applications accepted all year round
-
of agricultural weeds to herbicdes from an eco-evolutionary perspective. This project will develop models for the evolution of herbicide resistance that combines field data and computer models. The aim is to