Purpose:

To contribute to student success by delivering high-quality teaching, supporting curriculum development, and advancing research within the field of Fire Safety Engineering.

Education:

PhD in fire safety/protection engineering, mechanical engineering, or other relevant engineering disciplines.

Minimum Qualifications:

• Minimum 3 years of teaching experience in Higher Education.
• Demonstrated research experience with a strong publication record in relevant fields.
• Familiarity with the implementation of quality assurance policies and procedures is preferred.
• Membership in a relevant professional organization (e.g., MIFireE, AIFireE, MSFPE, PMSFPE).
• Certified Fire Protection Specialist (CFPS) is preferred.
• Industrial experience in fire safety engineering is preferred.

Skills:

• Ability to work effectively as part of a team.
• Excellent written and verbal communication skills.
• Strong time management and organizational skills.
• Accurate record keeping and documentation.
• Creativity, initiative, and enthusiasm in teaching and research activities.

Work Duties

The Assistant Professor position is designed to help the faculty member develop independence as both a researcher and an educator. The role primarily involves research, teaching, and professional development.

Key Responsibilities:

• Teach courses at the undergraduate and postgraduate levels.
• Conduct research within the subject area.
• Supervise student projects and theses.
• Actively seek external research funding.
• Collaborate with industry and engage with the wider community.
• Perform administrative tasks related to the duties listed above.

Type of employment/Contract: Full time position

Salary: Monthly

Number of positions: 3

Country: Oman

Interested applicants are requested to send their CVs to HR@icem.edu.om

Employer / Host Institution
Universitat Politècnica de Catalunya (UPC), CERTEC research group, Department of Chemical
Engineering, Barcelona, Spain

Supervision / PI
Dr. Ronan Paugam

Location
Barcelona, Spain

Sector / Type
Academic / Full time

Relevant Divisions / Fields
Atmospheric Sciences, Geosciences Instrumentation & Data Systems, Natural Hazards

Level / Experience
Experienced, Post-doctoral

Salary / Contract
Minimum €36,000 per year (gross), remunerated over 14 payments (12 months + two extras in
June and December).
Contract until 31 May 2028.

Required Education
PhD

Application Deadline
Open until the position is filled

Background

This position is part of the EUBURN project (Southern EUrope Biomass BURNing), which advances understanding of extreme wildfires in southern Europe, their climate and air quality impacts, and develops forecasting tools for direct (fire spread) and indirect (air pollution) risks. The project couples biodiversity, fire, and atmospheric processes across scales and aims to support operational services via measurement campaigns and open data sharing. A first campaign (SILEX) in July–August 2025 has tested instruments aboard the SAFIRE ATR-42 aircraft. A subsequent campaign (EUBURN-RISK) is planned in summer 2027, including drones, aircraft, ground platforms, and enhanced monitoring across France, Spain, and Portugal. Partner institutions include Météo-France (CNRM), AEMET, IPMA, University of Évora, UPC, and several other European and operational agencies.

UPC (CERTEC) focuses on fire observation, wildland and WUI fire behavior, fire modeling, and
risk analysis. The successful candidate will contribute to the fire observation component under
Dr. Paugam’s supervision.

Job Description / Tasks

The tasks of the post-doctoral researcher include:

• Process data from the 2025 SILEX campaign: compute Fire Radiative Power (FRP) using the midwave infrared (MWIR) camera aboard SAFIRE ATR-42, and compare with satellite sensors (e.g. VIIRS, SLSTR, FCI).
• Delineate fire fronts and compute Rate of Spread (ROS) from IR observations. Build on earlier methods developed by Dr. Paugam for helicopter observations; extend to operational scenarios. Fire front segmentation will be enhanced via machine learning, using EMA-INFACO’s data archive.
• Develop drone payload for fire activity observation during night flights: detect hotspot locations and intensities in real time. A key challenge is geolocating fire activity despite limited IMU accuracy; a spring 2026 prescribed burn in Andalusia will support required precision estimation, and a prototype will be tested in summer 2026 with EMA-INFACO.
• Facilitate integration of fire observation outputs with modeling teams within the EUBURN consortium.
• Assist in preparation and execution of the 2027 field campaign.
• Disseminate results via scientific publications, conference presentations, and stakeholder communications; contribute to project reports and internal workshops.

Candidate Profile / Requirements

• Holds a PhD in a relevant domain (e.g. remote sensing, fire science, atmospheric science, applied physics, engineering)
• Experience in satellite/airborne/infrared remote sensing, fire behavior monitoring, or data
  processing
• Skills in image processing, machine learning, geospatial data, and programming
• Ability to work independently, initiative in research, and effective communication skills
• Experience or interest in integrating observational data into modeling frameworks

How to Apply

Submit a CV and cover letter to ronan.paugam@upc.edu.

Applications are invited for a fully funded PhD scholarship (up to three years) in the Department of Civil and Environmental Engineering (University of Canterbury UC, New Zealand), under the main supervision of Dr Andres Valencia and co-supervision of academics of the fire engineering group at the UC. The project will be undertaken in close collaboration with Fire and Emergency New Zealand, who are providing funding support.

Description

Water is the most widely used agent for fire suppression, yet critical questions remain on how to optimise delivery methods for different fuels and scenarios. This PhD will focus on experimental and analytical investigations of water-based suppression systems, with emphasis on suppression performance, which can include:

• Firefighting hose streams: studying suppression against well-defined fires (e.g. known heat release profiles) and streams (e.g. well-known droplet distribution and flux).
• Sprinkler systems: studying dispersion and suppression against fires (e.g. known heat release profiles.) Characterizing dispersion flux.
• Emerging technologies (optional depending on additional funding and external collaborators): exploring the feasibility of drone-mounted water delivery systems for rapid initial attack.

The student will conduct laboratory-scale suppression experiments using fuels with clearly defined burning characteristics and heat release rate profiles. Experiments will be designed to expose fires to precisely characterised water fluxes, allowing quantitative assessment of suppression effectiveness. In parallel, the student will develop and apply analytical modelling frameworks (e.g., Valencia et al. 2021a and Valencia et al. 2021b) to interpret results and generalise findings beyond the lab scale. The results will directly support the development and refinement of standards and codes of practice, such as the NZ firefighting water supplies code (e.g. SNZ PAS 4509).

Expected outcome

The successful candidate will:

• Develop experimental protocols for assessing suppression effectiveness with hoses, sprinklers, and novel platforms.
• Quantify relationships between water application rate, droplet characteristics, and fire suppression outcomes.
• Provide data and insights to inform design standards, operational guidelines, and emerging technologies for fire services.
• Present results at leading international conferences and contribute to peer-reviewed publications.

Candidate Profile

Preferred applicants will have:

• A strong background in fire engineering, mechanical engineering, fluid dynamics, or related fields.
• Demonstrated experimental skills, ideally in combustion, heat transfer, or suppression systems.
• Strong analytical and coding ability (Python/Matlab/R) for processing experimental data.
• Interest in translational research that links laboratory experiments with real-world firefighting practice

Scholarship

Provider: Fire and Emergency New Zealand
Amount: NZD 32,000 per annum + domestic tuition fees (up to 4 years)
Location: Department of Civil and Environmental Engineering, University of Canterbury, Christchurch, New Zealand

Closing date: 7th November 2025. To apply, please send your CV, motivation letter and Transcript to Dr. Andres Valencia: andres.valencia@canterbury.ac.nz

The Department of Fire Protection Engineering at the University of Maryland, College Park (UMD),
invites applications for one Postdoctoral Research Associate position and one Ph.D. research
assistantship position in computational modeling of Wildland Urban Interface (WUI)
conflagrations.

The spread of wildfires into WUI communities and their progression into urban fires has severely affected
our communities, resulting in loss of life and extensive damage to properties and infrastructure. A key
element in reducing these impacts and developing effective wildfire adaptation strategies is the ability to
understand and accurately model wildfire spread to and within WUI areas. The main research goal for
both roles is to enhance the mathematical representation of thermal degradation of solid fuel sources and
flaming and smoldering combustion processes in fire simulators and to extend their use to modeling WUI
fires at large spatial and temporal scales. The project will primarily contribute to the advancement of the
open-source fire modeling ecosystems, such as Fire Dynamics Simulator (FDS), and apply the model to
further our understanding of WUI fire spread using high-resolution physics-based simulations.

Job Description

● Postdoctoral Position: The successful candidate will collaborate with our team to develop a
framework for urban fire modeling in FDS; more specifically, building a modular framework for
constructing the computational domain and implementing a firebrand generation module in the
solver. The efforts will involve the integration of large-scale datasets in a computationally
efficient paradigm to enhance the applicability of the simulators in WUI fires.

● Ph.D. Position: The successful candidate will work within a collaborative team to implement a
novel char oxidation and smoldering-to-flaming transition model and conduct high-resolution
simulations and validation studies of WUI fire scenarios.

Qualifications

● Expertise in thermal degradation of solid fuel sources and combustion (reactive flow) modeling.
● Strong background in computational fluid and fire dynamics.
● Demonstrated expertise in fire modeling using Fire Dynamics Simulator (FDS).
● Strong programming skills in FORTRAN; familiarity with CUDA is an advantage.

Appointment

● The postdoctoral position is an 18-month appointment, renewable for an additional 12 months
based on performance and availability of funding.
● The Ph.D. position is a 36-month appointment, renewable until graduation.
● The Department of Fire Protection Engineering at UMD fosters interdisciplinary collaboration
with academic and industry partners and is committed to advancing fire and wildfire science.

How to apply

Submit your application package via email before October 15 to Prof. Arnaud Trouve
(atrouve@umd.edu) and Ali Tohidi (atohidi@umd.edu).

Applications must include:
● A cover letter elaborating on your interests, expertise, and why you are a good fit.
● Your full CV, along with an official copy of your transcripts/degree
● Email addresses of at least three references.

Applications are invited for two fully funded (four years maximum) PhD scholarships in the department of Civil and Natural Resources Engineering (New Zealand), to work on overpressure events in combustible compartment under the supervision of Dr Andres Valencia, Dr Aatif Khan and Emeritus Prof. Charles Fleischmann in the context of the Project “Investigating Overpressure Events in Combustible Compartments” funded by Fire Safety Research Institute.

Description
Fires are inherently unexpected and unpredictable events that threaten both the occupants and firefighters. One of the most unexpected phenomena in a compartment fire is an overpressure event (OPE) that can occur with no apparent flammable liquid or gas within the building. Sometimes referred to as a smoke explosion or backdraft, an OPE represents a significant danger to firefighters operating within the building due to its explosive nature that can cause burns and traumatic injuries. In this project, OPE in combustible compartment will be experimentally investigated in the state-of-the art fire facilities at UC. The student will design and perform experiments to

1. Determine the ventilation openings factors and geometry that result in an OPE in a combustible compartment.
2. Evaluate the compartment conditions (temperature and gas species) immediately preceding an OPE.
3. Quantify the intensity of an OPE based on the volume of the external fireball, compartment pressure, and maximum opening velocity.
4. Evaluate the impact of vertical ceiling obstruction, i.e. joists/rafters, on the intensity of an OPE over a range of opening factors.

Selected candidates will attend technical conferences and symposia to present their research results.

It is preferred that the candidates demonstrate strong experimental background in fire engineering and strong analytical skills using Python/R/Matlab.
Scholarship
Provided by: Fire Safety Research Institute
Amount: $32,000 per annum + tuition fees (New Zealand Dollars)
Closing date: 13th October 2025.
To apply, please send your CV, motivation letter and Transcript to Dr. Aatif Khan: aatif.khan@canterbury.ac.nz

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

Supervision: Jean-Louis Consalvi¹, Dominique Morvan¹, Gilbert Accari³ & Pierre Boivin²

1 Aix-Marseille Université, IUSTI/UMR CNRS 7343, 5 rue E. Fermi, 13453 Marseille
Cedex 13, France
2 Aix Marseille Univ, CNRS, IUSTI, Marseille, France
3 Lebanese American University (LAU), P.O. Box 36, Byblos, Lebanon

Description of the Post Doc

This research project will be funded by the “Institute for Mechanical Engineering” of Aix-Marseille University. Once selected by the reaserch team, the project and the candidate will be evaluated in a sigle round in November, 2025 for a final decision in December, 2025. The Post Doc will last up to 24 months (12 months renewable once). He/she will benefit from an additional support budget of 5k euros/year (for symposium participation, international collaboration missions, small equipment, etc.).

This research project investigates firebrand spotting, a critical mechanism for fire spread in both wildland and wildland-urban interface (WUI) fires. Firebrands, which are flaming or glowing embers, are generated by burning vegetation or structures. These embers are then lifted by fire plumes and carried downwind, where they can ignite new fires or structures far from the main flame front [1]. Spotting significantly influences fire spread patterns because it acts over much longer distances than heat transfer mechanisms from flames to unburnt vegetation. In addition, it is estimated that more than half of the homes destroyed in WUI fires are due to firebrands [2].

The complex problem of spot wildfires can be broken down into three individual processes:

1. Firebrand generation and thermochemical state: How firebrands are produced and their initial chemical and thermal properties [3].

2. Transport and thermochemical evolution during flight [4].

3. Ignition upon landing: the initiation of smoldering or flaming combustion in a receptive fuel bed after the firebrand lands [1].

Stochastic models have been developed to model firebrand spotting [5, 6, 7]. These models are particularly well-suited for integration into operational fire spread models, providing real-time capabilities that are highly valuable for operational use and decision-making in fire management [7]. The stochastic spotting models rely on model parameters that are difficult to estimate and strongly depend on the weather (wind speed, ambient temperature, relative humidity) and the vegetation (type, moisture content).

This project research aims to combine CFD modeling of the spotting process with statistical learning methods to explore how the stochastic model parameters evolve with the most sensitive input data. The CFD simulations of the spotting process will be performed with FireStar3D, a fully physical, three-dimensional wildfire simulation model, co-developed at M2P2, the Lebanese American University and Toulon University. [8]. A particular fundamental focus of this project will be on developing ignition models for fuel beds by firebrands. This aspect is crucial as it represents the least understood of the three processes previously described in firebrand spotting.

Candidate profile

The desired candidate must hold a PhD related to combustion, fire research, and numerical simulation. The candidate will work in a research laboratory environment and will have to demonstrate autonomy, pragmatism, and a proactive approach.

• Contact and candidature: Jean-Louis Consalvi (jean-louis.consalvi@univ-amu.fr).

References

[1] A. C. Fernandez-Pello, Wildland fire spot ignition by sparks and firebrands, Fire Safety J. 91 (2017) 2–10.

[2] S. E. Caton, R. S. P. Hakes, D. J. Gorhan, A. Zhou, M. J. Gollner, Review of pathways for building fire spread in the wildland urban interface part i: exposure conditions, Fire Technol. 53 (2017) 429–473.

[3] S. L. Manzello, S. Suzuki, M. J. Gollner, A. C. Fernandez-Pello, Role of firebrand combustion in large outdoor fire spread, Prog. Ener. Combust. SCi. 76 (2020) 100801.

[4] N. Sardoy, J. L. Consalvi, B. Porterie, A. C. Fernandez-Pello, Modeling transport and combustion of firebrands from burning trees, Combust. Flame 150 (2007) 151–169.

[5] B. Porterie, N. Zekri, J. P. Clerc, J. C. Loraud, Modeling forest fire spread and spotting process with small world networks, Combust. Flame 149 (2007) 63–78.

[6] E. Mastorakos, S. Gkantonas, G. Efstathiou, A. G. b, A hybrid stochastic lagrangian – cellular automata framework for modelling fire propagation in inhomogeneous terrains, Proc. Combust. Inst. 39 (2023) 3853–3862.

[7] G. Efstathiou, S. Gkantonas, A. Giusti, E. Mastorakos, C. M. Foale, R. F. c, Simulation of the december 2021 marshall fire with a hybrid stochastic lagrangian-cellular automata model, Fire Safety J. 138 (2023) 103795.

[8] N. Frangieh, G. Accary, D. Morvan, S. Meradji, O. Bessonov,Wildfires front dynamics: 3d structures and intensity at small and large scales, Combust. Flame 211 (2020) 54–67.

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.