PhD Opportunities within Engineering Systems and Design

We have a large number of PhD students across the group, researching a wide variety of exciting and varied research topics

This PhD will investigate how to effectively use AI techniques to support the design of high-value system. Initial research will investigate the feasibility and potential benefits of AI at different stages of the design process. The main body of research will be concerned with the creation of an AI method(s) to support a particular stage of the design process.

Supervisor(s): Prof. Ben Hicks and Prof Aydin Nassehi or Dr. Chris Snider

Design for End-of-Life – Prof. Ben Hicks

This PhD will research and create tools and methods to improve the design of products for the circular economy. There are several directions that the project could take ranging from a methodology for optimal use of DfX tools for circularisation; augmentation of current CAD/modelling tools for emulating the impact of in-service life on end-of-life condition and treatment; and, incorporating technoeconomic assessment of circularisation early in the design process. The selection of pathway will depend on the individual’s interests, findings from literature review and discussions with our industrial partners.

Supervisor(s): Prof. Ben Hicks & Dr. James Gopsill

Drug release from medicated chewing gum (MCG) with computational methods – Dr. Kazem Alemzadeh

The development of MCG as drug delivery systems holds significant promise for enhancing the safety and effectiveness of medication administration. However, optimising the physical properties and design of MCG, as well as predicting drug release in the oral cavity, require a comprehensive understanding of the complex interplay between various parameters. This PhD research proposal aims to utilise virtual tools to simulate and predict drug release in MCG, thereby facilitating the optimisation and certification of these innovative drug delivery systems. Computational methods can complement the experimental work by exploring a larger parameter space and accessing quantities that are not easily measurable.

Supervisor(s): Dr. Kazem Alemzadeh & Dr. Nicolo Grilli

Know What You Eat: Miniaturised, non-invasive sensor systems for live monitoring of food’s true condition and nutritional value

Smart packaging is poised to revolutionize food monitoring. Equipped with sensors detecting freshness and harmful elements and powered by digital technologies like the Internet of Things (IoT) and AI to enable real-time data collection and analysis, smart packaging can boost product quality, safety, and distribution efficiency. Most commercial solutions still utilise limited sensor data, like temperature and humidity, thus overlooking the potential for advanced sensors to provide consumers with augmented information, like food’s real-time nutritional value. This project will harness novel sensor and digital technologies to create smart packaging sensor systems delivering augmented, “live” data about the food we consume.

Supervisor(s): Dr Maria Valero

Wearable, non-invasive sensor systems for overtraining monitoring in sport – Dr Maria Valero

Overtraining, a prevalent syndrome among athletes, emerges when training regimes surpass the body’s recovery capacity. Overtraining syndrome (OTS) symptoms often overlap with regular post-exercise discomforts such as soreness, fatigue, and strain, making early detection and assessment extremely difficult. Athletes with OTS suffer from lower fitness and performance levels, and increased risk of injury. This project aims to develop a new wearable sensor system for continuous, non-invasive monitoring of OTS risk for athletes. By providing real-time insights into training impacts, this novel system will help athletes to manage OTS risks for enhanced overall well-being and sports performance.

Supervisor(s): Dr Maria Valero

Data-Driven Sustainability in Advanced Manufacturing – Dr. Maria Valero

Advanced manufacturing, like composite and additive manufacturing, is called to drive the forthcoming green industrial revolution by engineering emission-free aircraft, carbon-neutral offshore wind turbines, and hydrogen storage solutions for renewable energy. However, advanced manufacturing processes are highly diverse and resource-intensive, thus requiring of comprehensive, well-founded data to understand and optimise energy and resource requirements. Combining in-process sensing techniques with digital technologies like the Internet of Things, AI, Big Data, this project will develop a novel system capable to collect and analyse process and resource data for the sustainable, advanced manufacturing processes of the next green industrial revolution.

Supervisor(s): Dr Maria Valero & Prof. Ben Hicks

Our team has recently been exploring the value of synchronising physical and digital models in VR worlds (i.e. 3D printing and CAD geometry), creating prototypes that simultaneously leverage the strengths of the physical and digital domains. This has highlighted the value of haptic interactions with products – like feel, force, and vibration – that are often not present until the prototype has been developed into later stages. This project will investigate how we can emulate haptic interactions with prototypes to give better understanding of product performance at earlier stages, and how we can use a combination of physical, digital, and haptic to create new ways of interacting with and learning from design prototypes.

Supervisor(s):  Dr. Chris Snider

Understanding how a user interacts with a product and how it completes its function or changes their behaviour is a critical process for the vast majority of products. While this process is often performed through qualitative testing and observational studies, recent advances in sensing and visualisation technologies give scope to use direct capture of user behaviours through low-cost means. This could allow data-driven interpretation of product performance, leading to better understanding of human factors, tailoring of products to individuals, or even self-optimising products. This PhD will investigate how data-driven capture of product behaviour could occur, and how to leverage data to generate best value.

Supervisor(s): Dr. Chris Snider

Recently emerging technologies like MR and VR are giving us new ways to experience the digital world, while rapid real-time sensing technologies are also giving us new ways to digitise the physical. In combination, these give potential for a paradigm shift in how we design, using both the physical and digital together to create new design realities. This PhD will investigate this paradigm, looking at these technologies when used together can change how we create.

Supervisor(s): Dr. Chris Snider

The core goal of many activities in the design process is to evaluate and learn about the artefact we are designing. This can be through physical testing, user testing, or a myriad of computational analyses. One challenge here is that many more detailed analyses are either computationally intensive or complex to set up and run, with the time that they take to implement delaying design and interrupting design cycles and limiting their use to specialists. This PhD will look at recent developments in AI, mixed reality, and sensing technologies to investigate how we can create useful, lightweight analyses at early design stages, that can be explored in real-time by users with a range of skill levels, all with a view to increase accessibility and accelerate the design process itself.

Supervisor(s): Dr. Chris Snider

4D printing multi-functional soft micro-robotic for minimally invasive surgery – Dr. Fengyuan Liu

4D soft robots for minimally invasive surgery, such as untethered microgrippers, is currently the most feasible method for integrating this technology. Since the internal human body structure is complex and reaching every nook and corner using conventional methods or materials is limited, advances in 4D soft robotics-based untethered microgrippers have gained attention as a significant development for more precise and minimally invasive surgery options. The existing soft robots do not allow for precise control and are able to complete a single movement which is not fully 4D printing integrated. The volume of existing soft robots is too large to apply on minimally invasive surgery. This project proposes to develop a soft robot using 4D printing technique and shape memory material to conduct a series of movement for minimal invasive surgery, including monitoring, drug delivery, eradicating blood clot or plaque.

Supervisor(s): Dr. Fengyuan Liu

Aerosol Jet printing of collagen for skin regeneration – Dr. Fengyuan Liu

The skin plays a vital role in several significant physiological functions, including wound healing. It is possible to regenerate the skin’s epidermis and dermis layers using bio-printed skin substitutes in patients suffering from skin injuries. Collagen bioprinting is the most promising strategy in skin regeneration. Most examples of collagen printing are extrusion methods that rely on collagen fibrillogenesis. However, the slow solution-to-gel transition of collagen fibrillogenesis allows time for a deposited print line to flow and spread away from its deposited position, which results in poor print resolution, and can make multiple layers difficult to achieve. Aerosol jet® printing (AJP) is a printing method that forms an aerosol from an ink and carrier gas and forces the aerosol to coalesce on a substrate via impaction. AJP has been investigated for the deposition of DNA, enzymes, collagen and silk fibroin with moderate success, but not yet applied to skin regeneration. This project will explore the aerosol Jet printing of collagen for skin regeneration.

Supervisor(s): Dr. Fengyuan Liu

This project tests the hypothesis that Cyber-physical production systems can be developed much faster using “non-identical digital twins”. This is achieved by creating a lightweight, low fidelity, digital twin to a physical system and co-evolving this twin, together with adjusting, the parameters in the real system. The co-evolution is enabled by learning from both the digital and the physical twins and shaped further by input from domain experts. Techniques such as machine learning and sensor data fusion are used together with automated rigs that emulate manufacturing processes to inform the research and allow the mechanical properties of the process to be assessed in both the physical and cyber environments.

Supervisor(s): Dr. Aydin Nassehi and Prof. Ben Hicks

Augmented and virtual reality systems together with a tangible user interface could allow complex system designers and existing facility managers to assess the effects of various decisions quickly.  This would enable experts to simulate different scenarios and create robust risk mitigation plans. This project aims to create a novel user interface and connect it to a multi method system simulation platform to propose a new immersive framework for design and improvement of value adding systems including factories, hospitals and airports.

Supervisor(s): Prof. Aydin Nassehi and Dr. Chris Snider

Explainable manufacturing operations via semantic analysis

In manufacturing, actions by humans, machines, or their collaboration are context-dependent. Semantic analysis discerns not just actions themselves, but their intent and context. In complex manufacturing settings, the contextual information generated by semantic analysis, helps in early error detection and workflow deviations, enables manufacturing operators to customise their actions, making their decision-making smarter and more tailored. This PhD project aims to enhance manufacturing operations by integrating semantic analysis with digital twin technology, focuses on developing a system that tracks, understands, and anticipates manufacturing operations to establish a smarter, safer manufacturing environment.

Supervisor(s): Dr. Qunfen Qi

Navigating Complex and Dynamic Information Flows in Digital Manufacturing

Digital twin technologies use real-time data from the physical system to analyse, predict, and control the system in an optimal way, enhancing manufacturing efficiency and accuracy, at the cost of increased design complexity in dealing with the big volume of sensed data, and complicated IT infrastructure to manage and process them. The PhD project is to develop formal tools for efficiently managing dynamic information flows within multi-level digital manufacturing models in production system. It aims to address the complexities of data flow, ensuring coherent and stable operations in smart manufacturing, thus overcoming the increased design complexity, and facilitating optimal production control.

Supervisor(s): Dr. Qunfen Qi

Advanced agent-based modelling of manufacturing system

This research aims to address the challenges in agent-based modelling (ABM) in manufacturing systems, such as computational complexity, scalability, and complex system interactions. This PhD project will focus on developing a novel framework for agent-based modelling in manufacturing systems, utilising the conceptual tools of category theory, specifically computational graphic rewriting. This approach is intended to offer a more scalable framework, facilitating the management of larger and more complex systems without the traditional exponential increase in computational requirements, making it easier to calibrate, validate, and integrate these models with existing manufacturing systems and software.

Supervisor(s): Dr. Qunfen Qi

The application of cognitive and physiological measurements to improve the engineering design process and team dynamics

Low-cost sensing and informatics are used increasingly to help people improve their performance whether it be in terms of physical training (eg WHOOP), sleep quality or gut health (eg Zoe). Engineering projects are complex, often time pressured, stressful and involve a large number of team members from a variety of backgrounds. Moreover, engineering project performance is of critical importance, with companies, careers and economies succeeding or failing based upon them. The ubiquity of smart watches as well as numerous low-cost and open-source sensing technologies provide ample opportunities for in-situ measurement of designers undertaking design in the wild.

This PhD will explore how low-cost sensing and associated analytics can provide insights into the engineering design process and enable process improvements through use of physiological and neurocognitive measurements. 

Supervisor: Dr. Mark Goudswaard

Transdiciplinary modelling of urban agriculture systems

As the world’s population gravitates increasingly towards cities, Urban Agriculture (UA) is increasingly considered as means of enabling food security in order to meet the sustainable development goal of making cities and human settlements inclusive, safe, resilient and sustainable. While UA is heralded to have wide ranging benefits for food security and urban sustainability, implementation is low as there is no established approach for how UA should be applied. 

Transdisciplinary systems modelling allows rapid iteration of real-world scenarios to provide evidenced guidance of how, in this case, UA should be utilized. These techniques have been used to upend manufacturing paradigms and similar approaches, if coupled with real-world data, could do the same for food production. 

This PhD will involve a mixture of transdisciplinary systems modelling and development of physical urban agriculture test beds in order to provide evidence based recommendations on how UA could and should be applied based on findings at scale (systems modelling) and from ‘grass roots’ technological and biological perspectives.

Supervisor: Dr. Mark Goudswaard

Unlocking the potential of AM

Additive manufacturing technologies in both metals and polymers afford enormous design freedoms stemming from their ability to make single products with multi-materials and optimise both external and internal geometries. Despite this huge potential, applications are limited by abilities to design appropriately for these manufacturing techniques. This PhD will explore the development and application of new design methodologies, for example, through applications of novel material combinations or design space exploration that could enable the creation of new and improved products.  

Potential applications could include: embedded circuitry and smart sensing; 4D printing; or, design for electric machines.

Supervisor: Dr. Mark Goudswaard

Design for inspectability in metal Additive Manufacturing 

Metal additive manufacturing has huge potential for improving products through the design freedoms and light weighting that it affords. Potential applications are limited by regulation and part qualification. Costs associated with validating and qualifying new manufacturing techniques result in manufacturers typically opting for business as usual and sticking with the status quo.

Validation of parts, structural or otherwise, can be enabled by a wide range of Non Destructive Evaluation (NDE) techniques, such as ultrasound. When coupled with AI techniques, these can enable highly informative and reliable evaluation protocols. The efficacy of these however is limited by the design of the part with regard to accessibility for inspection and proportion of inspectable internal structures.  This can be improved by considering Design for Inspectability (DfI) – design principles for embedding inspectability – earlier in the design process. 

This PhD will involve the development of DfI principles and applying them in the design of metal AM components to enable part qualification and validation

Supervisor: Dr. Mark Goudswaard and Dr Sergio Cantero Chinchilla

If you have any questions please email esdi-group@bristol.ac.uk.