ZAG is offering postgraduate training opportunities for young researchers. Research areas and mentors are presented below.

1. Assoc. Prof. Dr. Andrijana Sever Škapin

Research area: Sustainable use of resources in construction

Sustainable use of resources is an important aspect in construction, where society and industry are facing the problem of_CO2 emissions and significant energy consumption. The photocatalytic decomposition of_CO2 to energy-rich compounds offers both a solution to excessive_CO2 concentrations and the use of renewable energy sources (sunlight). In the field of photocatalysis, one project is currently underway at SM462 and one is due to be completed in 2024. The development of a method to evaluate photocatalytic_CO2 reduction using the existing GC-MS system will be one of the first tasks of the MR. In future work, MR will focus on the preparation of new materials based on _Nb2O5 and _Nb2O5-TiO2 heterostructures. By developing new photocatalytic materials and understanding the links between synthetic parameters and material properties, he will contribute to the development of the field with the aim of increasing the efficiency of photocatalysis and eventually transferring knowledge into practice.

4. Assoc. Prof. Andrea Lucherini, PhD.

Research area: Safety, resilience and adaptability of the built environment

The MR training programme develops highly qualified young researchers to carry out complex thermomechanical analyses of wood structures exposed to fire, in collaboration with world-leading institutions. The researcher will build on existing knowledge of wood ignition, heat transfer, pyrolysis, charring and flame propagation, which has traditionally only been studied under standard stove conditions. The key focus is on the decay and cooling phases of fire, which are often overlooked in fire design, but are crucial for timber structures. The aim of the programme is to develop advanced fire testing techniques using high performance radiant panels and reduced scale fire testing, with accurate quantification of temperature boundary conditions. The project will integrate structural analysis with multiphase computational models to improve performance-based fire design methodologies, leading to safer and more resilient timber buildings. The findings will support engineering guidelines and standards, benefiting researchers, engineers, architects and policy makers, while promoting wood as a sustainable building material and increasing the global competitiveness of timber construction.

The conditions for a young researcher are:

• has not completed a PhD or obtained the title of Doctor of Science,
• have a second-level degree in a relevant technical or natural science discipline,
• has not yet been employed as a young researcher,
• not more than 4 years have elapsed since the end of your studies; and
• meets the conditions for admission to a doctoral programme.

Description of works and tasks

• Carrying out the school requirements for the doctoral degree
• Carrying out research in projects under the guidance of a mentor
• Preparing your doctoral dissertation
• Demonstrated ability to carry out individual research independently
• Contributing to the drafting of terms of reference and opinions in the field of action
• Work on the certification of the conformity of construction products
• By order of the Supervisor, all the tasks for which he/she is qualified

More information and registration

A more detailed description is available on the website of the Employment Service of the Republic of Slovenia. To apply, you must be enrolled in a postgraduate programme in the academic year 2025/2026. Pending the selection procedure, a declaration that the candidate will graduate or nostrify his/her diploma by 15.9.2025 inclusive (documentation must be submitted to the ZAG by this date to be considered as valid fulfilment of the condition) will be sufficient in lieu of a copy of the diploma.

For more information, please contact the mentors: andrijana.skapin@zag.si and andrea.lucherini@zag.si.

Applicants are requested to indicate their registration number when applying. the posting on the ZRSZ, the job code and title of the post for which they are applying, and the field of research in which they are interested (as described).

Applications are invited until 15 August 2025 at kadri@zag.si.

AIDIGFIRE: Developing smart buildings fire safety management using an integration of artificial intelligence and digital twin technologies

https://www.bcu.ac.uk/architecture-built-environment-computing-and-engineering

This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.  

All applicants will receive the same stipend irrespective of fee status. 

Application Closing Date: 
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026. 

How to Apply 

To apply, please follow the below steps:  

1. Complete the BCU Online Application Form 
2. Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.  
3. Upload two references to your online application form (at least one of which must be an academic reference). 
4. Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s). 
5. International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores. 

Project title: Developing smart buildings fire safety management using an integration of artificial intelligence and digital twin technologies

Project Lead:  ​Dr Javad Hashempour​

Project ID:​ 13 – 46456414 

Project description:

​​Fires in high-rise buildings can be challenging and difficult to manage and suppress. In recent years, fires in high-rise buildings have caused disasters resulting in death and injury, such as the Grenfell Tower fire in London in 2017, where 72 lives were lost. Regardless of what caused the fire, the fire service faced difficulties in spotting fires in the external cladding, which led to delays in identifying the fire’s location and, more importantly, delays in shifting from a stay-put strategy to full simultaneous evacuation due to the evolving situation. Therefore, having integrated systems that alert the fire service to fire conditions and occupant locations, and allow monitoring of fire and smoke progression through the building, is extremely essential. 

​This project aims to address this issue by developing a coupled AI–digital twin system that displays essential building fire safety information and provides real-time predictions of fire and smoke conditions in the building, as well as occupant locations and the status of the evacuation process. These capabilities include extracting data from fire detection and protection devices in the building, monitoring evacuation routes, and estimating the number of evacuees and occupants remaining inside.  

​The project will utilise advanced fire and smoke dynamic simulations to train machine learning models to understand how fire and smoke behave in a building. Once trained, the models will be able to predict the likely spread of fire and smoke based on real-time sensor inputs such as heat or smoke detectors. In addition, evacuation conditions will be monitored by training machine learning based detection algorithms to count the number of occupants leaving the building and identify the locations of remaining occupants. 

​This information will be visually mapped onto a digital model of the building for easy viewing and interpretation. The resulting digital twin platform will continuously update and display the current fire conditions in the building. During an emergency, this innovative tool will provide firefighters and building managers with critical, timely information to support better and faster decision-making, ultimately improving building fire safety and firefighting efforts.​ 

Anticipated findings and contributions to knowledge:

​​This research is anticipated to deliver several important findings. The project will promote the use of building sensor data in machine learning–based real-time prediction models. It will establish the feasibility of using machine learning, trained on extensive fire simulation data, to rapidly and accurately predict the behaviour and spread of fire and smoke within high-rise buildings. 

​The significant contributions to knowledge include providing a novel, integrated approach to digital fire safety management. The developed digital twin will bridge gaps between static fire safety documentation, real-time fire detection and protection sensor data, and predictive modelling. This innovative framework will set a new benchmark for how fire safety information is managed, visualised, and utilised during emergencies. 

Moreover, the research outcomes will advance the theoretical understanding of data integration in digital twin platforms. It will demonstrate practical methods to enhance building fire safety practices, improve emergency response capabilities, and inform future building fire safety regulations and standards, supported by abundant data from real fire incidents.​

Person Specification:

​​We are seeking a highly motivated and capable candidate to support a project at the intersection of fire safety engineering, computational modelling, and emerging digital technologies. This position is ideal for graduates in computer science or computer/electronic engineering with experience in the application of machine learning. However, applicants with other relevant engineering backgrounds, such as fire engineering, mechanical engineering, architectural engineering, or civil engineering, with proven experience in applying machine learning will also be considered.

​While an MEng or MSc qualification in a relevant field is desirable, equivalent industry experience with a BEng or BSc will also be considered. The successful candidate should have applied programming skills to develop, train, and validate machine learning models, along with a sound understanding of algorithm selection, model evaluation, and the interpretation of results. The candidate must demonstrate strong analytical and problem-solving abilities, particularly in working with complex datasets and interpreting outputs within the context of the built environment and fire safety. 

​A working understanding of digital twin technology and its applications, such as how digital twins integrate simulation, monitoring, and real-time data to support improved decision-making, is desirable but not essential. The ideal candidate will have experience working across disciplines, including fire safety engineering, building engineering, and computational modelling. Strong communication skills, both written and verbal, are important, as the role involves presenting complex technical information to both technical and non-technical audiences. Collaboration and teamwork are essential; the candidate should be confident working independently as well as part of a multidisciplinary team.​  

Overseas applicants:

International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.

Contact:

If you have any questions or need further information, please use the contact details below: 

– For enquiries about the funding or project proposal, please contact: javad.hashempour@bcu.ac.uk 

– For enquiries about the application process, please contact: research.admissions@bcu.ac.uk

A novel combustion modelling approach for critical and transient phenomena in fire-driven turbulent diffusion flames: extinction, re-ignition, production of toxic species

See PDF flyer: https://iafss.org/wp-content/uploads/2025/07/PhD-position-in-Ghent-Belgium-.pdf

Motivation

Predictive modelling of compartment fires remains very challenging, due to the prohibitively wide spectrum of scales to be resolved in simulations of turbulent flames and two-way coupling of gas-phase combustion with gasification of combustible material. This project will advance both problems by developing, from revisited theory, a novel sub-grid combustion model (SCM) for unresolved gas-phase combustion phenomena in large-eddy simulations (LES). The SCM will be capable of capturing flame extinction and (re-)ignition accurately, enabling prediction of these critical transient phenomena in fires by inclusion of finite-rate chemistry. The novel SCM will be implemented in multiple software packages and validated against available experimental data. Its advantage over conventional models (infinitely fast chemistry) will be demonstrated. The SCM will be tested in a systematic manner for unconfined flames and then applied to predict transient development of under-ventilated enclosure fires. With respect to the latter, dynamics of burning rate will be thoroughly examined with and without full coupling between the gaseous flame and fuel gasification rate. While advancing the problem of gas-condensed fuel coupling, charring and non-charring combustible materials as well as liquid fuel evaporation together with complex heat and mass transfer will be considered, and the finite-rate chemistry effects in flaming combustion will be investigated. A new approach for production of toxic species (CO) in under-ventilated fires will be developed and validated.

Job description

You will be closely involved in implementation of the novel modelling concepts in commercial (ANSYS Fluent) and open-source (OpenFOAM and/or FDS) software and perform a very extensive and systematic CFD study, assessing and further developing the SCM. The flow chart gives an overview of the work packages.

Your profile

• You have an MSc degree in mechanical/thermal/chemical engineering or fire safety engineering.
• You have a strong interest in numerical simulations, combustion and fire modelling.
• You have proven experience in using ANSYS Fluent, with the ability to code and implement user-defined functions, or OpenFOAM, with the ability to modify existing and incorporate new code blocks.
• You have proven experience of numerical simulations in fluid dynamics, preferably in combusting flows and fires.
• You have good skills in written and oral communication in English.
• You are flexible, responsible and able to work independently as well as in a team.

What we offer

Ghent University (https://www.ugent.be/en) is one of the major universities in Belgium, and most of its activities take place in and around historic city of Ghent. You will work in an internationally well- recognized team with many years of experience in CFD simulations of fires. Project funding is guaranteed for the entire PhD period of 4 years.

How to apply

Submit your application via email before 21 July 2025 to Prof. Bart Merci (Bart.Merci@UGent.be), Dr. Georgios Maragkos (Georgios.Maragkos@UGent.be) and Dr. Alexander Snegirev (Alexander.Snegirev@UGent.be). Applications must include:

• A cover letter in which you specify why you are interested in the position and why you consider yourself a suitable candidate (800 words max).
• Your full CV, including a full transcript of records to date (complete degrees and grade lists).
• E-mail addresses of at least two reference persons.

https://www.findaphd.com/phds/project/wildfires-and-climate-change-physics-based-modelling-of-fire-spread-in-a-changing-world/?p183663

There are two PhD positions available. These PhD projects in fire science interlink prevention and prediction of wildfire risk, by contributing to the development of a fundamental physical model to understand the process of fire spread for wildfires, as part of a European Research Council grant (https://cordis.europa.eu/project/id/101161183 ).

Uncontrolled wildfires are a global phenomenon that are becoming more commonplace as changes in moisture and local temperature driven by climate change affect local fuel properties and ecosystems. Different vegetation distributions can lead to very different fire spread mechanisms, as well as different effects on structures. In these PhD projects the research will aim at quantifying some of these mechanisms for different wildfire scenarios. The project will likely require a combination of qualitative, quantitative and simulation methods.

Depending on the strengths and interests of the PhD candidates, the PhD projects will focus on some of the following aspects:

1. The quantification of the fundamental physical and kinetic differences arising from different vegetation fire types such as crown fires, shrub fires, and smouldering fires.
2. A methodology to link lab-scale and field-scale fires.
3. Numerical model of ignition with a database of fuel properties for various geographical regions.
4. A multi-physics model of the fluid dynamic and combustion interaction of fuels based on the effect of moisture, fuel distribution and fuel obstructions.

You will be part of an active research programme in the Heat and Fire Lab (https://heatandfire.github.io/ ) in the Department of Engineering under the supervision of Dr Francesco Restuccia. Our group is focused on carrying out experimental and computational multidisciplinary research in the thermal sciences covering heat transfer, combustion, fire science, and bioenergy. Our interests range from helping develop more efficient and durable energy storage to understanding the fundamentals of ignition and fire spread for prevention of damage to people, property, and the environment from unwanted fires in areas such as wildfire and electrification. Our current projects focus on wildfire dynamics, battery fires, thermal management of Lithium-Ion batteries, and ignition research.

Application Details:

To be considered for the position candidates must apply via King’s Apply online application system. Details are available at https://www.kcl.ac.uk/engineering/postgraduate/research-degrees

Please apply for Engineering Research (MPhil/PhD) and indicate Dr Francesco Restuccia as the supervisor and quote the project title in your application and all correspondence.

Please ensure to add the code [FIREMOD] in the Funding section of the application form.

Please select option 5 ‘I am applying for a funding award or scholarship administered by King’s College London’ and type the code into the ‘Award Scheme Code or Name’ box. Please copy and paste the code exactly.

The selection process will involve a pre-selection on documents and, if selected, will be followed by an invitation to an interview. Interviews will take place on a rolling basis with an expected start date of October 2025.

Further information can be found at https://www.kcl.ac.uk/study/postgraduate-research/how-to-apply

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Funding Notes

Stipend: Tax-free stipend of approximately £22,780 p.a. with possible inflationary increases after the first year.

Bench Fees: Research allowance for consumables, conferences and travel.

Tuition fees: UK tuition fees 25/26 £7,500 per year or international tuition fees 25/26 £32,400 per year.

These tuition fees may be subject to additional increases in subsequent years of study, in line with King’s terms and conditions.

Note: A UKRI fully funded studentship will only cover what is listed above. Applications should be aware there may be other costs which will not be covered by the studentship, for example, visa fees, healthcare surcharge, relocation costs

See website (1) https://wpi.studentemployment.ngwebsolutions.com/jobxJobdetailPrint.aspx?JobId=4861&win=True, (2) https://wpi.studentemployment.ngwebsolutions.com/jobxJobdetailPrint.aspx?JobId=4862&win=True

(1) We are seeking a highly motivated PhD student to join our research team focused on enhancing the resilience of Wildland-Urban Interface (WUI) communities against wildfire threats. The successful candidate will be responsible for conducting data integration and classification of fire exposure scenarios, utilizing databases and literature to build a comprehensive framework for WUI fire shelter design. The student will actively participate in the design process of a tiered fire shelter system, integrating Hazard Mitigation Measures (HMM) and Fire Protection Engineering (FPE) principles. The candidate will assist with computational modeling using the Wildland Urban Interface Fire Dynamics Simulator (WFDS), validating the shelter designs through simulations and experimental data. The ideal candidate should have a strong background in fire dynamics, computational fluid dynamics (CFD), or related fields, with experience in data analysis and modeling. This is an excellent opportunity to contribute to cutting-edge research aimed at developing new standards for fire shelters in wildfire-prone areas.  A Master’s degree in Fire Protection Engineering, Mechanical Engineering, or a related field is required.

(2) We are seeking a highly motivated graduate student to join our research group at Worcester Polytechnic Institute (WPI). The student will be conducting research on the topic “Near-field emissions and its relation to fire behavior”. The successful candidate will be responsible for conducting laboratory scale and field scale experiments (travel within U.S.) with the state-of-art measurement tools. The student will actively participate in the design and building of large-scale experimental setup for testing fire behavior. The student will collaborate with experts from federal agencies (USFS) and partner institutes (Univ. of Melbourne, UCLA etc.). The ideal candidate should have a strong background in the thermal sciences (fluid dynamics and heat transfer). This is an excellent opportunity to contribute to cutting-edge research aimed at understanding the fundamental coupling between fire behavior and emissions.

Location: National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA

Position type: 2-year term

Summary:

The Wildland-Urban Interface Fire Group of the National Institute of Standards and Technology(NIST) is seeking a motivated and talented researcher or recent graduate to join our team. This role will be focused on advancing the modeling of wildland fire dynamics and smoke transport, with a particular focus on prescribed fire and the wildland-urban interface. This position offers an exciting opportunity to contribute to cutting-edge research that will support the development of robust numerical models with the aim of improving the planning and implementation of prescribed fire.

Duties:

• Develop, implement, and evaluate numerical models of wildland fire dynamics, including combustion, heat transfer, and fluid flow. An emphasis will be placed on the Fire Dynamics Simulator (FDS) and improving its ability to model fire spread and near-field smoke transport in outdoor flows.
• Process and analyze geospatial field data to validate and refine numerical models, ensuring their accuracy and reliability.
• Work closely with an interdisciplinary team of scientists, engineers, and external partners to understand the required model capabilities to meet stakeholder objectives.

Desired qualifications:

• A PhD or equivalent experience in Atmospheric Science, Fire Science, Mechanical Engineering, Environmental Engineering, Computer Science, or a related field. Experience with topics related to wildland fire or fire science is preferred.
• Demonstrated experience in numerical modeling, particularly in fluid dynamics, combustion, or atmospheric transport processes.
• Proficiency in programming languages, especially Python, Matlab, and/or FORTRAN. Familiarity with computational fluid dynamics (CFD) software and tools, particularly making use of HPC systems.
• Strong analytical and problem-solving abilities as well as excellent written and verbal communication skills.

For more details please contact: Eric Mueller (eric.mueller@nist.gov)