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PhD Studentships

Edinburgh Napier University - School of Computing, Engineering and the Built Environment

Call for applications

Edinburgh Napier University is ranked the top modern University in Scotland in the 2022 Times World University Rankings. The School of Computing, Engineering and the Built Environment is highly regarded and has invested recently heavily in research in terms of both staff and facilities to conduct world class research in a wide range of disciplines.  

In the 2021 Research Excellence Framework (REF), our research was ranked top modern university in Scotland in terms of research power.

As part of our recent significant investments in research, we have recently recruited additional academics with outstanding research capabilities.

The investment in research is continuing with a large number fully funded 3-year PhD studentships being made available of which this is one. The studentship will cover full UK or international tuition fees and will include a standard living allowance at the RCUK rate (Currently £17,668 pa).

At this stage, we are recruiting students for following projects.

  • Algebraic Modelling of Molecular Interactions in Biological Cells
  • Development of a Knowledge-Based System for Smart Manufacturing Factory
  • Nature Inspired Optimisation/Learning - Area 1: Evolutionary Robotics
  • Nature Inspired Optimisation/Learning - Area 2: Meta-heuristic Search/Algorithm-Selection in Combinatorial Optimisation
  • Preparation of nanocomposite-based electrodes and development of flexible energy storage devices

A brief description of the projects is shown below. More information on requirements and how to apply is available following the provided links.

The studentship is expected to start in March or October 2023.  All applications must be received by 14th December 2022. Those who have not been contacted by 10th January 2023 should assume that they have been unsuccessful.

More information about PhD degrees at Napier can be found at www.napier.ac.uk/research-and-innovation/research-degrees.

For further details and how to apply please visit blogs.napier.ac.uk/sebe-research/fully-funded-phd-studentships-available-call-for-applications

Project Description

Nature Inspired Optimisation/Learning - Area 1: Evolutionary Robotics

Director of Studies: Professor Emma Hart (e.hart@napier.ac.uk)

The PhD project will explore optimisation, learning and/or adaptation in the context of evolutionary robotics. Possible avenues of research include the co-evolution of morphology and control of robots the interaction of evolution and learning mechanisms to produce bodies and behaviours that are specialised to specific environments and tasks. Alternative projects might focus on adaptation of behaviour only, using learning methods (e.g. evolution, reinforcement learning) to adapt controllers in real time to adapt to new environments, or learning repertoires of behaviours to enable robust performance.  Another promising area is in the use of state-of-the art methods from the quality-diversity literature to fully explore rich search spaces of both morphologies and controller.  Projects can be conducted in simulation only but there is also the possibility to utilise our robotics laboratory to conduct experiments on physical robots.

 

Nature Inspired Optimisation/Learning - Area 2: Meta-heuristic Search/Algorithm-Selection in Combinatorial Optimisation

Director of Studies: Professor Emma Hart (e.hart@napier.ac.uk)

Combinatorial problems are ubiquitous across many sectors. In a typical scenario, instances arrive in a continual stream and a solution needs to be quickly produced.  Meta-heuristic search techniques have proved useful in providing high-quality solutions, but it is challenging to select the correct solver for a particular instance and/or tune it to optimise performance. If the characteristics of instances change over time, it is also possible that at some future point, instances are sufficiently novel that there is no appropriate solver known or the selector is incapable of choosing the best algorithm. This project will focus on one or more aspects of tackling this issue; for instance developing novel algorithm-selection methods that are capable of selecting the most appropriate method; using  algorithm-generation methods (e.g. genetic programming) to generate or tune algorithms to work well on instances that occur in novel regions of the instance space; developing methods that are capable of learning from experience, i.e. continually improving selection methods or generation methods over time as knowledge is learned from solving past instances. The project is likely to mix techniques from meta-heuristic optimisation, automated algorithm generation and machine-learning, particularly borrowing ideas from the transfer learning or continual learning literature.

 

Algebraic Modelling of Molecular Interactions in Biological Cells

Director of Studies: Professor Peter Andras (p.andras@napier.ac.uk)

There has been active research over the last 60 years on modelling molecular interactions through various computational approaches (Maturana & Varela, 1980; Rashevsky, 1969; Rosen, 1991; Ganti, 2003; Banzhaf & Yamamoto, 2015). Fontana & Buss (1994) proposed a lambda calculus based approach to describe molecular interactions and molecules, while the proposal of Rosen (1991) offers an approach that is similar to category theory description of living systems. The PhD thesis of Elie Adam (2017) provides a very interesting category theory based approach to describe behaviour of interacting systems, while an earlier approach to use category theory for modelling systems is provided by Goguen (1992). Lambda calculus and category theory have close links (Scott, 1982), however these have not been explored much in the context of modelling of molecular interactions, with the exception of Andras (2011), which points to the importance of this link and suggests that appropriate models of molecular interactions must be infinite at the scale of categories.

The PhD project will aim to follow the approach in Andras (2011) and build on Fontana & Buss (1994) and on the category theory based modelling ideas in Adam (2017) to develop a new algebraic modelling of molecular interactions in biological cells. We will develop a modelling framework for molecular interactions in general first and then we will look at more detail at molecular interactions that happen within biological cells and aim to capture the difference between systems of molecular interactions that happen within cells and those that can happen without requiring biological cells. The modelling work will include both theoretical and computational modelling, aiming to find an appropriate computational implementation framework that allows the simulation of molecular interaction systems described by the theoretical modelling approach.

 

Preparation of nanocomposite-based electrodes and development of flexible energy storage devices

Director of Studies: Dr Libu Manjakkal (l.manjakkal@napier.ac.uk)

Flexible and conformable energy sources with high energy storage capability and fast charge/discharge rate are needed for applications including wearables, robots and electric vehicles. The power requirements for operation of components in these devices are in different capacity ranges. The above-mentioned applications require new features and designs that traditional energy storage technologies simply cannot provide. This could be either due to (i) toxicity (ii) rigid packaging and high weight (iii) low energy density and life cycle of flexible batteries. This PhD project will develop new flexible battery based on new nanocomposite-based anode and cathode. 

The anticipated major activities:       

  • Formulate and synthesize nanocomposites for anode and cathode.
  • Characterize the structural and electrochemical properties of materials and devices.
  • Fabricate and characterize the flexible and conducts its application for wearable system

 

Stochastic Randomised Control on Bicycle Balance

Director of Studies: Dr Keng Goh (k.goh@napier.ac.uk)

Bicycles are widely used for transportation, exercise, and recreation and play an important role in urban mobility. Individuals benefit from the fact that cycling is a healthy and cheap form of transport. Moreover, in urban areas, cycling can sometimes prove to be faster than other transport modes and also allows cyclists to avoid traffic jams. For society, the advantages of cycling include environmental sustainability, cheap infrastructure requirements, and improvements in public health. However, there are some challenges for the existing bicycles in the market including:

  1. Bicycle is statically unstable, especially for old and less flexible people.
  2. Bicycles commonly are subjected to various sources of disturbances, making bicycle control more challenging.

This project is to develop a randomized control algorithm to address the aforementioned issues. The randomised controller will be designed firstly in theory and then be implemented on a real bicycle to test. This control method will be based on fully probabilistic design where the control goal is to keep the bicycle stay balance while subjecting various source of randomness. Then, the developed controller will be implemented on a bike provided in the lab, while different sensors will be used to collect the signals and a motor to provide the torque. This project is suitable for people who have basic control theory knowledge and electrical electronic knowledge. The applicants should have good experimental skill, mathematical skills and programming skills. The c programming skill is desirable but not essential.

 

Development of a Knowledge-Modelling Decarbonisation of Transport in Rural Areas

Director of Studies: Dr Stathis Tingas (e.tingas@napier.ac.uk)

The decarbonisation of the transport sector (primarily through electrification) has become one of the priorities of most advanced economies nowadays, including the UK, in the fight against climate change. This transformation is usually supported by national legislation that aims to facilitate the transition to more environmentally friendly technologies. For instance, the UK has recently mandated the ban on all new petrol and diesel car sales by 2030.

Optimisation approaches have been studied extensively in urban setups, however, there is limited understanding on the required approaches and appropriate technologies (e.g., types of charging stations, charging vs H2 refuelling stations, etc) to be used in rural setups, where the demands and the available solutions will be widely different. This becomes of paramount importance for countries like Scotland that are predominantly rural.

The objective of this work will be to develop appropriate models for establishing networks of charging and/or refuelling stations appropriate for rural areas, using evolutionary and machine learning algorithms. These models will need consider among others, the requirements of the distribution network (centralised vs decentralised), the possible revenue, the available public transport, the convenience of the users, the renewable energy sources, the local geography and others.

 

Development of a Knowledge-Based System for Smart Manufacturing Factory

Director of Studies: Dr Gokula Vasantha (g.vasantha@napier.ac.uk)

Many manufacturing parameters, such as the movement of input raw materials to machine performance, impact the productivity of a manufacturing factory. Therefore, manufacturers are interested in developing data-driven tools and techniques to monitor manufacturing units in real-time and develop smart, proactive strategies to improve performances. This doctoral research project aims to develop a knowledge-based system that assesses manufacturing factory performance, develops improvement strategies, and evaluates the impact of proposed improvements. The project focuses on the following research objectives: (i) automate collecting and analysing real-time factory data, (ii) predict adaption required in a smart manufacturing factory based on real-time information, and (iii) develop a knowledge-based system for providing automatic suggestions to improve manufacturing performance.

Since knowledge discovery to improve manufacturing factory performance is the core objective, this research requires an excellent understanding of manufacturing systems, system engineering principles, data analytics, and machine learning (i.e., predictive modelling) techniques. Furthermore, the research involves a complete data processing cycle, such as multi-modal manufacturing data collection with appropriate sensors (e.g. worker’s movement, machine temperature and vibration), data integration, data cleaning and data transformation. Therefore, it would be ideal if the PhD candidate has some experience either in big data analytics or system simulation modelling software such as SimUl8 and advanced programming skills.

The research work will initiate within the Flexible Manufacturing Laboratory at Edinburgh Napier University. The development of an early Knowledge-Based System will then be studied and tested in an actual manufacturing industry. The researcher joining this project will develop and train in the appropriate technical areas. The researcher will be actively encouraged to present the work at leading international conferences and workshops. The researcher should have an appetite for undertaking an enquiring and rigorous approach to research together with a keen intellect and disciplined work habits. The researcher will benefit from collaborating with Professors at the University of Edinburgh and Strathclyde through an ongoing EPSRC (The Engineering and Physical Sciences Research Council, UK) funded research project.

 

Adaptive Robotic Behaviours in dynamic and outdoor settings

Director of Studies: Dr Leni Le Goff (L.LeGoff2@napier.ac.uk)

In controlled settings such as factories, robots are able to achieve many tasks efficiently and accurately. However, it is still a challenge to enable robots to operate in unstructured, dynamic and outdoor environments. In such settings, changes can occur that can render the skills and knowledge of the robot ineffective. Robots must therefore be able to adapt previously learned behaviours to new tasks and settings. The approach proposed to be investigated is in two steps. First, existing resource intensive algorithms1 will be applied to learn robust behaviours and perceptual representation for the robot to tackle complex tasks and environments. In this first step the environments will be static. Then, light weight algorithms2,3, e.i. with fast convergence, will be explored to adapt quickly these learned behaviours and representations to face dynamic environments. The ultimate goal of this project is to enable robots to achieve complex task in outdoor settings4 where the conditions can change suddenly or progressively. Using mobile legged robot such as dog or hexapod robot, the Ph.D. work will focus first on testing the viability of the methods in simulation before eventually testing them on a real robotic platform.

Evolutionary Robotics: Generating diverse and functional robots by jointly optimising their body-plan and controllers

Director of Studies: Dr Leni Le Goff (L.LeGoff2@napier.ac.uk)

Evolutionary Robotics is a field of research studying evolutionary algorithms (EA) to optimise both the controllers and the body-plan of robots. The goal of using EA to generate body-plans is to find the optimal fit to purpose robot for a series of tasks and environments. This kind of algorithms is called Morpho-evolutionary algorithm (MEA)1. However generating the body-plans alone is not realistic as to evaluate the viability of a robot it has to achieve a task. In this way, machine learning techniques are combined with EA to generate behaviours using the generated body-plans. Such algorithm is then called Morpho-evolutionary algorithm with learning (MEL)2. The goal of this Ph.D. project will be to tackle the challenges raised by MELs such as generating a diverse set of body-plans, efficiently learn new controllers for unknown body-plans and the problem of transferring knowledge  through generation of robots. For this topic, the Ph.D. work will be done mainly in simulation but it will be possible to use real robots. A software and hardware framework to jointly optimise the body and controllers of real robots developed by the ARE project will also be available3. The framework is based on C++ for the software and 3D printing and hand-designed modules for the real robot.

Qualification Type: PhD
Location: Edinburgh
Funding for: UK Students, EU Students
Funding amount: £17,668 per annum
Hours: Full Time
Placed On: 25th November 2022
Closes: 16th December 2022
 
   
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