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(CPRD) and Hospital Episode Statistics to identify activity related to the treatment of community acquired pneumonia. This will require identifying relevant treatments and their associated Health Resource
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occurrence, aetiology, outcomes, and real-world impact. The student will be supported by a supervisory team including Professors in hepatology and epidemiology, and experts in computational statistics and
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sequencing, qRT-PCR, western blotting, bioinformatics, ELISA, cell culture, statistics, and literature reviews. A working knowledge of these techniques is therefore desirable. We will compare subtypes, detect
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performance and green innovation. It also explores how these effects change when combined with environmental regulations and innovation incentives. The project will employ statistical analysis and econometric
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statistics, social sciences, health economics, health psychology, and will be expected to complete a PhD during the award period. Guidance notes can be found below, along with details of the specific research
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changes in tinnitus impact. This project will require large scale data collection across multiple settings and platforms, and interrogation of the data using a range of advanced statistical techniques
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PhD Studentship: Leverhulme Trust PhD project: Lawn grass microbial fuel cells for widespread energy
statistical skills. Beyond scientific expertise, the program hones teamwork, critical thinking, and time management, offering scholarly exchanges within a vibrant PhD community. This comprehensive skill set
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relevance and acceptability, and senior statistical and design support to accelerate clinical translation. To view a list of projects available please click here You need a 2:1 or higher undergraduate degree
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activity (with a choice of either EEG or the newly developed OPM-MEG technique). In addition, you will be trained to statistically analyse your data with “off the shelf” (e.g. SPSS) and customisable software
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sponsors own security checks prior to the commencement of the PhD. Vision We are seeking a motivated PhD candidate with enthusiasm to learn about state-of-the-art developments in statistical machine learning