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Heriot-Watt University

Country: United Kingdom

Heriot-Watt University

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993 Projects, page 1 of 199
  • Funder: UKRI Project Code: EP/E026362/1
    Funder Contribution: 1,339,280 GBP

    The ISSUES Project Team believes the KT-SUE project is a major opportunity to make an important bank of knowledge readily accessible and usable to a variety of end-users who are tackling the implementation of sustainable development. Academic research has a major roll to play in facilitating the evidence-base for such sustainable development activity, and this project will enable the SUE research to reach a targeted and influential audience and achieve an appropriate impact in sustainable development policy making and management.The ISSUES Project Team have structured their proposal to ensure that policy makers, practitioners and other interested parties will be able to access, learn about and make use of the knowledge that emerges from the 'Sustainable Urban Environments' (SUE) research conducted by the EPSRC SUE consortia. When enacted, the proposal will provide channels between researchers and end-users so that knowledge can flow both ways, and so that future SUE research is informed by the every day reality that sets the context for end-users. The key features of this proposal are as follows:1. An extremely high-calibre, experienced team with staff from both Heriot Watt and Cambridge Universities. The team spans all domain knowledge relevant to the project, and will bring substantial academic and industrial experience to the work. The project team will be led by Professor Paul Jowitt with support from Professor Peter Guthrie, both of whom have international reputations in the field of sustainable development.2. A robust, comprehensive coverage of the area, and an approach based on proven Knowledge Management tools used by leading professional institutions such as the Institution of Civil Engineers, and adapted to provide an exact fit for this project and for the KT-SUE consortia.3. A four-stranded methodology which includes:a. Assessing the current status and transferability of existing research and knowledge, and assessing the current ability of policy makers and other end-users to seek out and make use of such knowledge.b. Making links between researchers and practitioners so as to establish communication channels and 'knowledge communities'. Alongside this, a Knowledge Transfer implementation plan will be developed. c. Implementation of the programme of Knowledge Transfer activities, with input from industry and from communication experts as required. This will provide two-way communication between researchers and practitioners, to ensure that knowledge is transferred in both directions for the benefit of each group.d. Evaluation and assessment of the KT work carried out. In addition, the potential of different KT methods for use in ongoing EPSERC work will also be evaluated.4. The method will include a survey and assessment of existing KT methods as well as investigating and evaluating new and novel KT methods5. Strategic and effective use of personnel, so as to gain maximum value from each team member.6. This proposal has the support of a number of external consultancies and bodies, as well as the approval of our listed referees.7. Our approach will ensure that the value of the research work carried out by the KT-SUE consortia is understood, appreciated and used by practitioners and other end-users. In addition to the above points this proposal will provide lasting value for related future research funded by ESPRC. It will do this through the evaluation of the effectiveness of different KT methods for use with sustainability related research and practitioners. Findings from this project could then be used as a platform for any subsequent research into the most effective methods of knowledge transfer in this type of field.

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  • Funder: UKRI Project Code: 2201047

    This is a Phd Research project in Physics. The nonlinear absorption of ultrafast laser pulses can be used directly write localised structural modifications inside dielectric materials. These modifications can be used to fabricate structures such as optical waveguides, volume gratings and lenses. This iCASE studentship, which is part funded by Renishaw, will focus on developing beam shaping techniques that can decrease the fabrication times involved in ultrafast laser inscription based manufacturing, with the aim of advancing this powerful platform into real-world manufacturing.

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  • Funder: UKRI Project Code: 1972019

    This project proposes research in the field of computational complexity theory, applying this field to increasing the efficiency of computer vision problems. Applications of computer vision such as video object tracking for autonomous vehicles and security systems require real-time processing of complex tasks. Research into improving the efficiency of computer vision algorithms could have an impact on these time-sensitive applications and allow for more complex computation to be done in real time

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  • Funder: UKRI Project Code: EP/J015180/1
    Funder Contribution: 1,051,760 GBP

    The fundamental challenges for signal processing are: how best to sense; how to distribute the processing and communication of the data within the network to maximize performance and minimize cost; how to analyze it to extract the salient information. Signal processing is the glue which holds together much of modern technology. The algorithms underpinning mobile communications, medical imaging, image rendering for games and many other technologies were all developed within the global signal and image processing research community. Today the world is an environment of pervasive interconnected sensing with the associated requirement to extract useful information from the large volumes of data that arise. In applications of defence, homeland security and environmental monitoring there is a need to collect and combine data from a range of sensors of widely differing complexity (e.g. from satellite imaging to ground based motion detectors) to achieve persistent wide area monitoring of a scene of interest. This can assist in the assessment of threats, e.g. the planting of improvised explosive devices, the long-term ecological effects of deforestation, or the monitoring of time critical events such as devastation by fire or flood. On the roads the external monitoring of traffic flow by closed circuit television networks, junction-based pressures sensors and GPS create an opportunity when combined with on-vehicle sensors (e.g. lidar, radar and video) to provide driver assistance and ultimately automatic driving systems. This Platform proposal seeks funding for a foundation for our research team in addressing these challenges.

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  • Funder: UKRI Project Code: 2517845

    This is a PhD research project in Mechanical Engineering. Biological tissue often has unique three-dimensional hierarchical microstructures and micro-morphologies, constantly evolving through their development and patho-physiological stages. For solid tumour, e.g. in forms of prostate, breast, skin and colorectal cancers, understanding the evolutions of their tissue microstructures is key to their clinical diagnosis. In addition, since the mechanical behaviours, in terms of both solid and fluid ones, highly depend on the tissue microstructures, it becomes possible to utilize the mechanical properties of the cancerous tissue for diagnosis purposes, through quantitative understanding of its structure-property relationship. This PhD project will employ medical imaging and processing techniques, 3D CAD, Finite Element Method and other advanced mathematical and numerical methods, such as homogenisation method (in its asymptotic or numerical forms), in order to quantify the structure-property relationship in various types of solid tumours. This project will benefit from data from existing and on-going work in the area of cancer mechanics in Tissue Mechanics Group, led by Dr Yuhang Chen, in various forms of mechanical characterisations of prostate/colorectal tissues. Although the main goal of this project is to develop novel computational approaches, it is also expected that certain experimental data (such as mechanical characterisation of soft tissue) may be required to validate the proposed modelling approaches.

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