Labour productivity is 0/L, where Q is output and L is the labour input - an input that has both a quantity dimension (man hours) and a quality dimension (skills). Human capital contributes to the skills of workers and is widely recognised to be the primary driver of Lin developed countries. This is reflected in labour productivity, and thence real wages. It is a widely-held view that Britain needs to increase the skills of the workforce, and ensure that they are better matched to employers' needs, to address the "skills gap". While the dominant proxy for productivity in the literature remains (hourly) earnings (i.e. the wage rate), reflecting the tendency for labour markets to better reward those with greater skills, there are good reasons for thinking this is a naive measure - an issue which will be explored in the planned research. Early research on the aggregate impact of education on GNP failed to reflect the findings in individual level studies - that showed large causal effect on wages, this is now attributed to measurement error in the aggregate data on education. In particular, subsequent research in [4] showed that correcting for measurement error bias results in aggregate data leads to effects on per capita GNP that exceed the individual level effects of education. Many countries have sought to increase their average living standards through the expansion of higher education. In the spirit of skills being the most important long-term driver of labour productivity, we aim to analyse the effects of higher education on wages. An excellent recent overview of the skills and productivity literature in Economics can be found in [3] and the role of skills in heavily emphasised in OECD's recent "forward look" in [5].

Many fascinating quantum behaviours occur on a scale that is intermediate between individual particles and large ensembles. It is on this mesoscopic scale that collective properties, including quantum decoherence, start to emerge. This project will use vibrating carbon nanotubes – like guitar strings just a micrometre long – as mechanical probes in this intermediate regime. Nanotubes are ideal to explore this region experimentally, because they can be isolated from thermal noise; they are deflected by tiny forces; and they are small enough that quantum jitter significantly affects their behaviour. To take advantage of these properties, I will integrate nanotube resonators into electromechanical circuits that allow sensitive measurements at very low temperature. First, I will study the motional decoherence of the nanotube itself, by using it as the test particle in a new kind of quantum interferometer. This experiment works by integrating the nanotube into a superconducting qubit, and will represent a test of quantum superposition on a larger mass scale than ever before. It will answer a longstanding question of physics: can a moving object, containing millions of particles, exist in a superposition of states? Second, I will use the nanotube device as a tool to study superfluid helium 3 – the mysterious state of matter that may emulate the interacting quantum fields of the early universe. By measuring an immersed nanotube viscometer, I will be able to measure the behaviour of superfluid excitations on a scale where bulk superfluidity begins to break down. Third, I will add to the device a nanomagnet on nanotube springs, creating an ultra-sensitive magnetic force sensor. This offers a way to perform nuclear magnetic resonance on a chip, ultimately creating a microscopy tool that could image for example single viruses.

There are numerous data sources to advance scientific knowledge, yet there are a greater amount of avenues uncertainty and trust to be brought into question. Therefore, the primary aim of this research is to understand how users of secondary data come to place trust in these contemporary sources - that have not been collected by themselves and may contain potential uncertainties, and how we can foster well-placed trust in these sources. - How do researchers view trust in data? Are they cognizant of the trust and uncertainty issues of data? Having seen from the literature that the definition of trust varies, how do researchers define trust in this context? - How trust is performed versus how it is verbalised. What do researchers actually do when they use these data sources? Are they apprehensive and take caution in these scenarios, or do they take a pragmatic approach and utilise this data (regardless of trust) if it is a necessity? - How do researchers account for potentially uncertain data in methods and methodologies? I.e. as uncertainty or untrustworthy data could potentially affect results and should be accounted for in order to be rigorous and reproducible. Are researchers aware of the effects of uncertainty and ambiguity in data and data science techniques? - Under which conditions is this data sufficiently trustworthy enough for the purposes that one might hope to use it? Are there varying contexts and purposes for this? - Finally, I will seek to understand the communication of uncertainty and trust. What forms of supplementary information are necessary for data users to formulate trust, and to use this data in their research? How can this be effectively presented and communicated?

Practitioners and professionals working in the public sector, particularly those in education, health and social care and local government, need to have a thorough understanding of the social determinants of health, and the magnitude and consequences of social inequalities in health, if they are to be successful in addressing them. This PhD study will examine awareness and understanding of health inequalities among final year university students intending to embark on careers in the public sector and whose professional responsibilities will have the potential to reduce health inequalities. The study will also critically evaluate the extent to which undergraduate and postgraduate curricula address health inequalities. Background The presence of social inequalities in health in the UK has long been recognised (Black, 1980). Evidence suggests that health status is influenced by a wide range of 'social determinants of health', including education; health care; the quality of living and working conditions; income; and 3 behaviours (Dahlgren and Whitehead, 1991). To improve population health and tackle health inequalities, action needs to be taken at different levels and across different sectors (Department of Health, 2009).