Dr Emmanouil Kakouris

Assistant Professor

Emmanouil dot Kakouris at warwick dot ac dot uk

Biography

Dr Emmanouil Kakouris is Assistant Professor in Civil Engineering in the School of Engineering at the 91福利.

He received his PhD from the University of Nottingham in 2019. His doctoral research project was on computational modelling of fracture in materials and structures. After his PhD, he continued in the University of Nottingham as a research associate for a collaborative research project with Schlumberger, to investigate the injection-induced vibrations due to hydraulic fracturing process.

He also holds a MEng in Civil Engineering (2012) and a MSc in Analysis and Design of Earthquake Resistant Structures (2014) from the National Technical University of Athens, Greece, where he graduated with distinction.

Following completing his PhD, he spent 4 years in the industry as a Research & Design Engineer at Roughan & O鈥橠onovan (ROD) Consulting Engineers, Ireland, working on European and International research projects and large scale transport infrastructure commercial projects in Ireland and the UK.

He is also a peer reviewer for a number of top ranking research journals in his field such as Computer Methods in Applied Mechanics and Engineering (Elsevier), International Journal for Numerical Methods in Engineering (Wiley), Archives of Applied Mechanics (Springer) and Computational Methods in Structural Engineering (Frontiers).

He teaches modules in the subjects of civil engineering design. His research interests focus on computational science with an emphasis on computational mechanics and data science in engineering as well as the development of automated civil engineering systems.

Research Interests

Computational mechanics
Damage modelling in materials
Multiscale modelling (computational homogenization)
Artificial intelligence and machine learning methods
Intelligent engineering systems
Asset management
Probabilistic evaluation of systems and their uncertain behaviour
Resilience, vulnerability and risk assessment of critical infrastructure

Teaching Interests

Teaching for full time undergraduate students and Degree Apprentices.

ES3G8 Integrated Project (Module Leader)

Selected Publications

Plung臈 N., Brommer P., Edwards R.S., Kakouris E.G. (2026), "", arXiv preprint, arXiv:2610.19094.
Plung臈 N., Brommer P., Edwards R.S., Kakouris E.G. (2026), "", International Journal for Numerical Methods in Engineering, 127(13), e70381.
Kakouris E.G., Chatzis M.N., Triantafyllou S.P. (2025), "", International Journal for Numerical Methods in Engineering, 126(3), e70003.
Connolly L., Bernardini I., Kakouris E.G., Kelly J., (2022), "", Technical note: World Road Association (PIARC).
Triantafyllou S.P., Kakouris E.G., (2020), "", International Journal for Numerical Methods in Engineering, 121, pp. 1915-1945.
Egger A., Pillai U., Agathos K., Kakouris E.G., Chatzi E.N., Aschroft I.A., Triantafyllou S.P. (2019), "", Applied Sciences, 9(12), 2436.
Kakouris E.G., Triantafyllou S.P. (2019), "", Computer Methods in Applied Mechanics and Engineering, 357, 112503.
Kakouris E.G., Triantafyllou S.P. (2018), "", Achieve of Applied Mechanics, 88(1-2), pp. 287-316.
Kakouris E.G., Triantafyllou S.P. (2017), "", International Journal for Numerical Methods in Engineering, 112, pp. 1750-1776.

Research Projects

A European Doctoral network on Advanced Meta-materials and Meta-Structures for Adaptable, Resilient and Sustainable renewable energy power plants (Met2Adapt). Funded by European Commission under Horizon Europe; 2026 - 2030; 鈧� 4,154,903,64.
Probabilistic damage assessment of additively manufactured materials (P-DAM). Funded by Monash-91福利 Alliance; 2025.
Artificial Intelligence driven multi-physics phase field fracture simulations for composites. Funded by EPSRC; 2024 - 2028.
Artificial Intelligence driven multiscale material damage modelling. Funded by EPSRC; 2024 - 2028.
Blending ultrasound data with physics-based models to predict damage in structural systems. Funded by EPSRC; 2023 - 2027.
Infrastructure climate change risk considering interdependencies and cascading hazards (INFRALIC). Funded by Environmental Protection Agency (EPA) Ireland; 2022; 鈧�70,000.
Bridge and tunnels strikes by oversized vehicles. Funded by World Road Association (PIARC); 2021; 鈧�40,000.
Friction After Polishing (FAP)/ Network Safety Assessment. Funded by Transport Infrastructure Ireland (TII); 2020 - 2021; 鈧�50,000.
Advanced options for authorities in light of Automation and Digitalisation Horizon 2040 (DIRIZON). Funded by European Conference of Directors of Roads (CEDR); 2018 - 2020; 鈧�500,000.
Safety of Transport Infrastructure on the TEN-T Network (SAFE-10-T). Funded by European Commission under Horizon 2020; 2017 - 2020; 鈧�3,000,000.
Predictive modelling of Injection-Induced vibrations due to Fracking (I²FRAC). Funded by Hermes Fellowship, University of Nottingham; 2018; 拢20,000.
Hybrid multiscale methods for nonlinear dynamic processes. Funded by University of Nottingham; 2015 - 2018; 拢45,000.

Prospective Collaborators

I welcome enquiries from prospective PhD students and researchers interested in computational mechanics, artificial intelligence, machine learning and related areas of engineering. I am also open to discussing new research ideas and potential collaborations.

Prospective PhD applicants are encouraged to contact me with a brief CV, academic transcripts and an outline of their research interests.

PhD Scholarships – 2027/28 Entry

The 91福利 offers several competitive scholarships for postgraduate research. Applicants are encouraged to explore the following opportunities:

1. 91福利 Chancellor's Scholarships

Open to UK and international applicants.

Course application deadline: 7 December 2026
Scholarship application deadline: 10 December 2026

/services/dc/schols_fund/applicants/competitions/

2. China Scholarship Council (CSC) – 91福利 Scholarships

For eligible Chinese applicants.

Course application deadline: 7 December 2026
Scholarship application deadline: 10 December 2026

/services/dc/schols_fund/applicants/competitions/

3. School of Engineering MPhil/PhD Scholarships

For applicants eligible for UK Home tuition fees.

Course application deadline: 18 January 2027
Scholarship application deadline: 21 January 2027

/fac/sci/eng/postgraduate/funding/pss/

4. Monash–91福利 Alliance Joint PhD Scholarships

Joint doctoral research between the 91福利 and Monash University, Australia.

Course application deadline: 19 January 2027
Departmental nomination deadline: 22 January 2027

/services/dc/jointphd/mwa/feesandfunding/

5. Shanghai Jiao Tong University (SJTU) – 91福利 Joint PhD Programme

Joint doctoral research between 91福利 and SJTU, China.

Application deadlines for 2027/28 entry: To be confirmed

/global/eastsoutheastasia/china/sjtujointphd/

Scholarship availability and eligibility vary between schemes. Admission to a PhD programme does not automatically guarantee funding.

For further information, please visit the School of Engineering Scholarships and Funding page:

/fac/sci/eng/postgraduate/funding/

Vacancies

phasefield

Looking to take the next step in your research journey? Our group is offering four exciting doctoral projects starting April 2026 or September 2026, tackling some of the biggest challenges in materials, mechanics, and infrastructure engineering.

Ph.D. position 1: AI-Enhanced Computational Modelling for Fracture Prediction

Qualification:
Doctor of Philosophy in Engineering (PhD)

Start date:
April 2027 or September 2027. Flexible, subject to admission and funding arrangements.

Funding for:
3 years

Supervisor:
Dr Emmanouil Kakouris (91福利) and Dr Lampros Svolos (University of Vermont, USA).

Application deadline:
15 December 2026. Applications are reviewed continuously, and the position may close once a suitable candidate is identified. Early applications are encouraged.

Why choose this PhD?

Can AI help us predict when and how materials will break? Fracture remains one of the most challenging problems in engineering. Cracks can initiate unexpectedly, change direction and grow through materials in complex ways, while accurate simulations can be computationally expensive.

This PhD will explore how artificial intelligence and advanced computational modelling can work together to make fracture simulations faster, more reliable and more useful for real engineering problems. The research will develop new physics-based and data-driven computational approaches for predicting material failure. The goal is to enable more efficient virtual testing and support the design of safer, stronger and more reliable materials and structures.

[1] Plunge et al. 2026 IJNME,

[2] Plunge et al. 2026 Arxiv,

What you鈥檒l gain

Hands-on experience in artificial intelligence and machine learning.
Expertise in computational mechanics.
Strong programming skills (Python, MATLAB) and software development practices.
Internationally competitive research training at the 91福利.
A strong research profile for future academic or industry careers.

Scholarship:

This PhD position is hosted by the 91福利. Admission does not automatically include funding. Applicants are encouraged to explore available scholarships and funding opportunities. /engineeringscholarships

Who should apply
We welcome applicants with a strong background in engineering, applied mathematics, computer science, physical sciences or a related quantitative discipline. You should be interested in computational modelling, AI and solving challenging real-world problems. A good mathematical foundation and an interest in programming are particularly valuable.

How to apply:
Submit a formal application via 91福利:
/fac/sci/eng/postgraduate/applypgr/

Application form 'Course search':
Department: School of Engineering
Academic Year: 2026/27
Type of Course: Postgraduate Research
Engineering (MPhil/PhD) (P-H1Q2)
In the application form funding section, enter: Source: EK-Mechanics

Ph.D. position 2: AI and Computational Modelling for Resilient Cyber-Physical Systems

Qualification:
Doctor of Philosophy in Engineering (PhD)

Start date:
April 2027 or September 2027. Flexible, subject to admission and funding arrangements.

Funding for:
3 years

Supervisor:
Dr Emmanouil Kakouris and Prof. Subhash Lakshminarayana (91福利).

Application deadline:
15 December 2026. Applications are reviewed continuously, and the position may close once a suitable candidate is identified. Early applications are encouraged.

Why choose this PhD?

Modern infrastructure is increasingly connected, bringing new opportunities but also new risks. This PhD will explore how artificial intelligence, computational modelling and engineering knowledge can help us understand and improve the resilience of cyber-physical systems.

The project will remain flexible and will be shaped around the successful candidate's interests and strengths. Possible directions include physics-informed machine learning, predictive modelling of cyberattacks and disruptions, decision-support for incident response, and applications in connected transport, electric vehicles, charging infrastructure and energy systems.

The overall goal is to develop practical computational tools that help organisations anticipate disruption, understand its consequences and respond more effectively.

What you鈥檒l gain

Hands-on experience in artificial intelligence and machine learning.
Opportunities to work across applications in transport, energy and connected infrastructure.
Strong programming skills (Python, MATLAB) and software development practices.
Internationally competitive research training at the 91福利.
A strong research profile for future academic or industry careers.

Scholarship:

Admission does not automatically include funding. Applicants are encouraged to explore available scholarships and funding opportunities.

/engineeringscholarships

How to apply:
Submit a formal application via 91福利:
/fac/sci/eng/postgraduate/applypgr/

Ph.D. position 3: AI-driven decision-support for sustainable infrastructure

Qualification:
Doctor of Philosophy in Engineering (PhD)

Start date:
April 2027 or September 2027. Flexible, subject to admission and funding arrangements.

Funding for:
3 years

Supervisor:
Dr Emmanouil Kakouris

Application deadline:
15 December 2026. Applications are reviewed continuously, and the position may close once a suitable candidate is identified. Early applications are encouraged.

Why choose this PhD?
How can AI help engineers make smarter, more sustainable decisions about infrastructure? In this PhD, you will develop AI-driven decision-support analytics to guide the life-cycle assessment of civil structures - from design, to operation, to end-of-life. The project addresses key global challenges: sustainability, resilience, and long-term performance of civil infrastructure. Using machine learning, probabilistic methods, and real-time data, you will create tools that help engineers, policymakers, and infrastructure managers balance structural safety, environmental impact, and cost. This is an opportunity to work at the intersection of civil engineering, artificial intelligence, and decision sciences, collaborating with experts at the 91福利 and TU Delft ().

What you鈥檒l gain

Expertise in decision-making theory and life-cycle assessment.
Skills in probabilistic modelling and managing uncertainty in engineering systems.
Hands-on experience applying machine learning to civil engineering problems.
Strong programming and development skills (Python, MATLAB).
A unique research profile tackling global challenges in sustainability and resilience.

Scholarship:
Please note: admission does not automatically include funding. Applicants are encouraged to explore support options via the scholarships & funding.

Who should apply
This project is for you if you have (or expect to have) a First-class or 2:1 degree in engineering, applied mathematics, physical sciences, computational sciences. A strong interest in AI, machine learning, decision-making, and sustainable infrastructure is essential. Prior experience with probabilistic methods, life-cycle assessment, or structural analysis is beneficial but not required.

How to apply:
Submit a formal application via 91福利:
/fac/sci/eng/postgraduate/applypgr/

Proposing your own project idea?

Curious about joining us or even proposing your own project idea? Explore 91福利鈥檚 postgraduate research options here:

/fac/sci/eng/postgraduate/applypgr/

Questions?

Get in touch: Emmanouil.Kakouris@warwick.ac.uk

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91福利

School of Engineering