HWUM James Watt PhD Scholarships
The HWUM James Watt Scholarships offer a life-changing opportunity for potential PhD students to work on most pressing global challenges from an Asian perspective.
The global challenges humanity faces today require a global approach to address them. For 200 years, Heriot-Watt University has been the home for innovators and future shapers who made the world a better place through research and education.
Promoting a thriving, flourishing research community within the globally connected University having campuses in UK, Dubai and Malaysia, we are committed to provide practical and sustainable solutions. We would like to invite you to join our community of research leaders and help us make a difference and create a positive impact.
The HWUM James Watt PhD Scholarships aim at attracting qualified candidates who wish to contribute towards the sustainable world development and address three key topics in Malaysia, including:
- Assessing the impact of positive education in the higher education sector
- Achieving Net-Zero emissions through sustainable and smart technology
- Developing a sustainable ageing society through the provision of future welfare, high-quality services, and innovative solutions
“Join us for #TogetherWeCan change the world”
We seek to provide up to six (6) PhD students at Heriot-Watt University Malaysia with full tuition fees and a generous stipend, offering them with great potential the opportunity to work within their chosen research field with the support of an excellent supervisory team consisting of at least one supervisor from the Malaysia campus and at least one supervisor from the Edinburgh campus.
It should be noted that these prestigious scholarships are extremely competitive with high eligibility requirements, and involve participation in a competition for very limited places.
The start date for funded places is in October 2021.
Successful candidates will receive the following financial support for up to 36 months:
- Full funding for tuition fees
- A stipend of RM36,000 per annum to assist with living costs
- A consumables/professional services fund of RM5,000 per annum.
- A programme of workshops and events delivered by Heriot-Watt’s Research Futures Academy.
- Applications are accepted from talented candidates from Malaysia and worldwide.
- Candidates must hold a first class undergraduate degree or equivalent.
- Candidates with a standalone Master’s qualification must have achieved a distinction.
- Prior to applying, candidates must have made contact with a supervisor at Heriot-Watt University Malaysia who has agreed to supervise them in their selected research project. Please review the James Watt PhD Scholarships – Research Projects (below) with Supervisor's Contact page for more information.
You should submit your application for admission to study at Heriot-Watt University Malaysia through our online admissions system, which will require you to provide:
- Degree transcripts for undergraduate and Masters study
- CV with two referees and their contacts (at least one should be an academic referee)
- A personal statement (max 2 pages), which must outline your academic and research achievements to date, indicate the research project that you are applying for, and explain why you are interested in this project and what are your career aspirations post-PhD)
Candidates please indicate ‘’James Watt PhD Scholarship Scheme” as the Name of Sponsorship or Sponsor under the funding section within the online application form.
Review and Selection
Applications will be reviewed by a two-stage process:
- Schools will review all the applications and select a shortlist of the very best candidates to present to the HWUM Selection Committee for consideration.
- The HWUM Selection Committee will make the final decision. The committee consists of the Director of Research & Enterprise, an Associate Head of School and two members from the Research Management Committee.
Up to six (6) students will be selected from the final interview by our Selection Committee.
The HWUM JWS application deadline for the 2021 entry is 31st August 2021. Candidates who apply by 31st August 2021 and are awarded a scholarship will be notified in September 2021.
Find out more
If you have any questions regarding the scheme please contact MyScholarship@hw.ac.uk
James Watt PhD Scholarships – Research Projects
Development of a sustainable nutrients value chain for healthy ageing among older adults
Most developed and developing countries worldwide are facing massive demographic change. By 2050, the number of people in Asia aged 65 or over is expected to grow to 937 million people, which is more than double today's number (1). Ageing has many effects on human capacity. Sustainable food and nutrients supply to fulfil and maintain a nutrient-dense diet for an ageing population are important (2). With advancing food technologies, the ability to extract, isolate and concentrate bioactive compounds from foods will spawn the development of functional food-based products and nutraceutical supplements. Therefore, the current food supply chain needs to be modified to fit the future requirement of ageing population while ensuring that resource and environmental constraints are met (3). The aim of the PhD project is to develop a superstructure optimisation model designing to achieve cost efficiency in a nutrients value chain of an ageing society, from food production, through processing management and product development, to a supply chain. This project would suit candidates with a good Master’s and/or Bachelor’s degree in Engineering especially Chemical Engineering.
Programme: Engineering and Engineering Trades
Main supervisor: Prof Ir Denny Ng, School of Engineering and Physical Sciences, Malaysia Campus (firstname.lastname@example.org)
Secondary supervisors: Dr. John Andresen, School of Engineering and Physical Sciences, UK Campus, Dr. Ng Lik Yin and Dr. Viknesh Andiappan, School of Engineering and Physical Sciences, Malaysia Campus
Evidence-based methods for developing critical skills in university students: feasibility, implementation and evaluation
Positive education enables students to attain wellbeing as well as academic excellence. Two defining features of a positive education institution are: i) using well-validated interventions, and ii) assessing improvements in well-being and reductions in ill-being as a result of those interventions. A number of validated interventions form an integral part of our compulsory Year 1 courses, but there is less consistent and widespread follow through of positive education practices into our discipline-specific teaching. Examples of validated interventions that could be amenable to the more traditional academic curriculum include conceptualising, synthesising, applying and evaluating information as a guide to beliefs and actions, and using effective heuristics to solve theoretical and practical challenges. The aim of the PhD project is to evaluate whether selected positive education interventions lead to well-being benefits and academic grades and to identify what works for whom in which circumstances. It would suit candidates with a good first degree in Psychology, or with a career aspiration in education/pedagogy. A Master’s qualification is desirable, but not essential.
Programme: Social & Behavioural Science
Main supervisor: Prof Deborah Hall, Professor of Positive Psychology, School of Social Sciences, Malaysia Campus (Deborah.Hall@hw.ac.uk)
Secondary supervisors: Dr Chia Ping Lee and Dr Ron Salden, Department of Psychology, School of Social Sciences, Malaysia Campus and Dr Terry Lansdown, School of Social Scienes, UK campus
Bringing coaching psychology into academic practice: feasibility, implementation and evaluation
Positive education enables students to attain wellbeing as well as academic excellence. Two defining features of a positive education institution are: i) using well-validated interventions, and ii) assessing improvements in well-being and reductions in ill-being as a result of those interventions. Coaching tools and techniques can be used by staff and students to create a powerful learning environment. This can be achieved by developing evidence-based coaching skills and competencies through the institution. This project is timely because our university is seeking to expand the role of the year 1 ‘impact coach’ to that of ‘personal tutor’. This initiative is currently envisaged as an informal coaching scheme (i.e., not delivered by staff with any formal coaching qualification). The objective is to develop an effective and sustainable set of evidence-based coaching skills and competencies for our local practice. The project would suit candidates with a good first degree in Psychology, or with a career aspiration in education/pedagogy or coaching. A Master’s qualification is desirable, but not essential.
Main supervisor: Prof Deborah Hall, Professor of Positive Psychology, Malaysia Campus (Deborah.Hall@hw.ac.uk)
Secondary supervisors: Dr Ke Guek Nee, Dept of Psychology, Coaching Psychology, School of Social Sciences, Malaysia campus and Dr Dasha Grajfoner, Coaching Psychology, School of Social Sciences, UK campus
A holistic approach for developing an optimum structural solution for Engineered Geopolymer Composites (EGC) using Machine Learning
Engineered Geopolymer Composite (EGC) is a new cement-less binder incorporating two distinct material technologies, namely Geopolymer and Engineered Cementitious Composite (ECC). It creates a low-carbon cementitious material with excellent strain-hardening characteristics and high tensile strain capacity in excess of 3%. Many research works on EGC focus on material development. For real applications, there are still many unanswered questions, especially about the structural behaviour and performance. This PhD project aims to take a holistic approach to develop an optimum design solution for EGC. Patterns of change in material efficiency, structural optimization, and embodied carbon emission of the EGC structural members will be analysed. Machine Learning (ML) will be used to predict the structural responses of the EGC members based on data generated from validated finite element model (FEM) and experiment. Predictive models developed will generate an optimum EGC design satisfying material efficiency and low-carbon emissions, thus providing a cost-effective solution for minimising impact to the environment and supporting practical applications of EGC. Candidates will be required to demonstrate a thorough knowledge of concrete technology, structural concrete design, structural analysis, and/or mechanics of material. A good MSc or MEng in Civil/Structural Engineering is desirable. Candidates with substantial research experience are highly encouraged to apply.
Programme: Engineering & Engineering Trades
Main supervisor: Dr Teo Wee, School of Energy, Geoscience, Infrastructure and Society, Malaysia Campus (T.Wee@hw.ac.uk)
Secondary supervisors: Dr Benny Suryanto, School of Energy, Geoscience, Infrastructure and Society, UK Campus and Dr Md Shabbir Hossain, School of Energy, Geoscience, Infrastructure and Society, Malaysia Campus
Autonomous aerial-based visual monitoring of energy assets under constrained environments
Inspection and monitoring of energy assets are primarily carried out using traditional methods such as human patrols and helicopter assisted surveys, which are slow, costly, and dangerous. These methods are also considered environmentally unfriendly. Recent years have witnessed autonomous monitoring becoming a reality with the proliferation of unmanned aerial vehicles (UAVs) or drones, spurred on by modern computer vision and deep learning algorithms. Nevertheless, there are several challenges that warrant attention: Firstly, power transmission towers are cluttered with power lines where faults in small components often go undetected in different environmental conditions. Secondly, existing methods utilise very heuristic based processing or learn models from siloed data that are collected from single sources. The aim of the PhD project is to design AI algorithms for automatic visual assessment of energy asset components and to build software toolkits and services for drones in collaboration with HWUK and industry partners. It would suit candidates with a career aspiration in energy or artificial intelligence. Candidates should possess a first-class honours in Computer Science or Electrical/Electronic Engineering, and a suitable Master’s qualification. They should be well-versed with coding. Prior knowledge in machine learning is desirable, but not essential.
Programme: Engineering & Engineering Trades
Main supervisor: Dr. John See, School of Mathematical and Computer Sciences Malaysia Campus (J.See@hw.ac.uk)
Secondary supervisor: Prof. David Flynn and Prof. Yvan Petillot, School of Engineering and Physical Sciences, UK Campus
Deep learning methods for assessing interacting dynamics of COVID-19 vaccination and non-pharmaceutical interventions (NPIs) in presence of heterogeneous risk factors
Vaccination attributes for SARS-CoV-2, such as coverage and efficacy, depend on a variety of heterogeneous risk factors such as individual disposition, socio-economic and health status, and regional and age-based characteristics. Although SARS-CoV-2 vaccination is ongoing, it will take time before herd immunity is achieved. Meanwhile, non-pharmaceutical interventions (NPIs) provide the necessary measures for stemming rising cases, over-hospitalizations and deaths, but they are detrimental to the economy and people’s livelihoods. It is, therefore, crucial to acquire a quantitative understanding of the interacting dynamics of vaccination and NPIs in presence of governing risk factors. This is achieved via epidemiological models that incorporate heterogeneity, vaccination dynamics and NPIs which are capable of predicting disease trends and the necessary NPI controls under on-going vaccine deployment. These models can be used to ascertain the time needed to achieve herd immunity and other important aspects of policy implementation on disease control while maintaining livelihoods. The aim of the PhD project is to develop statistical models and deep learning methods for assessing the joint dynamics of vaccination and non-pharmaceutical interventions for COVID-19 in presence of heterogeneous risk factors. The methods developed will be used to control cases and protect livelihoods for the overall well-being of society. This project would suit candidates with a first-class bachelor's degree in Statistics, Computing, or a related quantitative discipline with a career aspiration towards data science and deep learning. A good master's qualification is desirable but not essential. Advanced programming skills in R and Python, and familiarity with related deep learning libraries and packages are desirable.
Main supervisor: Dr Sarat Chandra Dass, School of Mathematical and Computer Sciences, Malaysia Campus (S.Dass@hw.ac.uk)
Secondary supervisor: Prof. George Streftaris, School of Mathematical and Computer Sciences, UK Campus and Dr Bhuvendhraa Rudrusamy, School of Engineering and Physical Sciences, Malaysia Campus