Find Academic Supervisors

Viknesh AndiappanView profile

Associate Professor

• Net-Zero Energy Supply Chains
• Net-Zero Energy Systems
• Process Integration & Optimisation
• +3 more

Dr. Viknesh Andiappan serves as Associate Professor in Chemical Engineering and Associate Director of the School of Research at Swinburne University of Technology, Sarawak Campus, within the Faculty of Engineering, Computing and Science. His academic foundation includes a PhD and MEng (Hons.) in Chemical Engineering from The University of Nottingham. PhD in Engineering (The University of Nottingham) MEng (Hons.) in Chemical Engineering (The University of Nottingham) His research pioneers net-zero energy systems through mathematical optimization, focusing on supply chain decarbonization, industrial symbiosis, and sustainable agriculture planning. Recent work integrates carbon accounting with palm oil value chains and biomass-to-energy systems, emphasizing practical implementation in Southeast Asian contexts. Analysis of his 15 most recent publications (2020-2022) reveals a dominant focus on decarbonization strategies (47%), biomass supply chains (27%), and process optimization (26%), with growing emphasis on circular economy applications and multi-stakeholder resource allocation frameworks. Key recognitions include: IBAE Young Researcher of the Year Award IChemE Young Researcher Malaysia Award Finalist (2018, 2019) Heriot-Watt University PRIME Award Finalist (2021) He actively supervises research students in net-zero energy systems while leading industry-relevant projects like the Ministry of Plantation Industries white paper on palm oil recovery strategies. His scholarly service includes editorial roles for Process Integration and Optimization for Sustainability and Frontiers in Sustainability. Though no dedicated lab is specified, his research group operates through Swinburne's School of Research, leveraging institutional facilities for computational optimization and sustainability analysis.

Suhyb SalamaView profile

Associate Professor

• Remote sensing of water quality
• Hydrological and water-resources management
• Radiative transfer modelling
• +6 more

Suhyb Salama is Associate Professor of Remote Sensing of Water Quality and Water Resources Management at the Department of Water Resources, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, the Netherlands. His research integrates radiative-transfer physics, machine-learning analytics and multi-sensor satellite data to unravel hydrological and water-quality processes from micro-scale spectroscopy to meso-scale land–atmosphere interactions, directly supporting UN Sustainable Development Goals 6 and 14. Education: PhD in Civil Engineering, Katholieke Universiteit Leuven, Belgium (2003) MSc in Hydraulic Engineering (magna cum laude), KU Leuven (1999) BSc in Civil Engineering, Damascus University, Syria (1993) Research interests: Salama’s scholarly work spans remote sensing of biogeophysical variables, spatiotemporal modelling of suspended sediments and algal blooms, radiative-transfer theory across visible to microwave domains, development of smartphone-based water-quality apps, detection of floating plastic litter, and assessment of groundwater–vegetation feedbacks. He combines optical, thermal and microwave observations with physical and data-driven models to deliver actionable information on water scarcity, pollution extremes and climate-change impacts. Recent publication trends (2023-2025): His latest articles focus on machine-learning quantification of estuarine sediment variability, advanced detection of floating plastics, smartphone calibration for citizen-based water-quality monitoring, spectral-textural mining mapping, and analytical derivation of water-clarity time series from Sentinel-2 imagery. Collectively these works push toward operational, open-source solutions for high-frequency, basin-scale water-quality monitoring and UN SDG reporting. Editorial & professional service: Salama serves as topical editor for the journal Remote Sensing (2020-2024) and has chaired sessions on long-term earth-observation of suspended particulate matter. He actively collaborates across Europe, Africa and Asia on projects that merge remote sensing, hydrodynamic modelling and capacity development. Teaching & capacity building: He teaches MSc courses on optical and microwave remote sensing for water resources, supervises MSc and PhD research, and champions problem-based learning that equips students with enterprising skills to tackle complex water and climate challenges.

• Combinatorial Optimization
• Operations Research
• Artificial Intelligence
• +3 more

Corinne LUCET-VASSEUR is a University Professor at Université de Picardie Jules Verne (UPJV), leading Research Unit UR 4290 (OCIA - Optimisation Combinatoire, Images et Applications). Her office (Room 302, Tel: 5900) serves as the hub for her research group focused on combinatorial optimization and artificial intelligence applications. Her research spans: Combinatorial Optimization : Developing metaheuristics for NP-hard problems Healthcare Logistics : Patient flow optimization, facility location, simulation training Logistics Engineering : Parcel distribution, vehicle routing with time windows Algorithm Design : Ant Colony Optimization, Adaptive Large Neighborhood Search, portfolio methods She applies these methodologies to solve complex real-world problems, particularly in healthcare systems where resource constraints and scheduling complexity demand innovative optimization approaches. Her work bridges theoretical advances with practical implementation through industrial partnerships. Current research projects include: SMILE PICK UP (CIFRE industrial partnership) Simusanté (healthcare simulation) LORH (logistics optimization) These projects secure ongoing funding and provide doctoral training opportunities through industry collaboration. Her publication record demonstrates consistent methodological innovation applied to healthcare and logistics challenges across multiple European conferences and journals. Professor Lucet-Vasseur actively mentors junior researchers through co-authorship on conference papers and journal articles. Her supervision style emphasizes practical problem-solving with industry relevance, preparing students for both academic and industrial careers in optimization. The OCIA research unit provides a collaborative environment for tackling complex combinatorial problems with real-world impact. The OCIA laboratory serves as UPJV's center for combinatorial optimization research, specializing in metaheuristic development for healthcare and logistics applications. The lab maintains strong industry connections through CIFRE contracts and applied projects, ensuring research relevance while providing students with exposure to real business challenges. Current focus areas include adaptive algorithm selection using reinforcement learning and fitness landscape analysis for optimization problems.

John McCallView profile

Professor

• Data Science
• Artificial Intelligence
• Nature-inspired Computing
• +5 more

Professor John McCall is a distinguished academic and researcher at Robert Gordon University's School of Computing, Engineering & Technology, where he previously served as Head of School. He currently serves as Director of the National Subsea Centre, leading initiatives to accelerate energy transition through smart technologies applied to industrial and environmental challenges in subsea and related marine sectors. With over 25 years of research experience in nature-inspired computing and artificial intelligence, Professor McCall has established himself as a leading expert in optimization algorithms and explainable AI. Professor McCall's research interests span data science, artificial intelligence, nature-inspired computing, and optimization, with significant applications in energy transition and subsea technologies. His work bridges theoretical foundations with practical implementations, having founded two spinout companies that deliver real-world optimization solutions to industry. He leads both the Complex Optimisation Research Group and the Computational Intelligence Research Group, where his team explores cutting-edge approaches to solving complex computational problems. Analysis of Professor McCall's recent publication record (2023-2025) reveals a strong focus on explainable AI, particularly in the context of evolutionary computation and metaheuristics. His research demonstrates an increasing emphasis on practical applications in energy systems, transportation, and subsea technologies, reflecting his commitment to addressing real-world challenges related to climate change and industrial transformation. The interdisciplinary nature of his work is evident in publications spanning computer science, operations research, renewable energy, and transportation planning. Lead of the Computational Intelligence Research Group ResearcherID: G-1423-2011 Scopus Author ID: 36797474900 ORCID: https://orcid.org/0000-0003-1738-7056 Professor McCall is actively involved in mentoring the next generation of researchers, currently supervising multiple PhD students across diverse topics including explainability of non-deterministic solvers, optimization of electrical machines, and computational intelligence applications in hydrocarbon systems. His research is supported by numerous grants from industry and government sources, with projects totaling millions of pounds focused on solving challenges in energy transition and smart technologies. At the National Subsea Centre, Professor McCall leads a multidisciplinary team working on digital twin technologies, subsea AI applications, and data-driven solutions for the energy sector. His work emphasizes collaboration between academia and industry to develop transformative solutions that address both current challenges and future opportunities in the subsea domain.

• Dynamic Optimisation
• Optimisation under Uncertainty
• Combinatorial Optimisation
• +7 more

Dr. Joan Alza Santos is an early-career Research Fellow at the School of Computing, Engineering, and Technology within Robert Gordon University (RGU). She is affiliated with the Complex Optimisation Research Group and contributes to RGU's research themes in "Environment, Energy & Sustainability" and "Living in a Digital World." Her work bridges academic research in dynamic optimization with practical applications in sustainable logistics and other real-world domains. Dr. Alza Santos completed her educational journey with: BSc (Hons) in Computer Engineering from Universidad del País Vasco (University of the Basque Country), 2013-2017 Postgraduate Certificate from Robert Gordon University, 2018-2021 PhD in Computing from Robert Gordon University, completed in January 2025 with no corrections required - a rare distinction Her research focuses on addressing fundamental challenges in dynamic optimization. Dr. Alza Santos has made significant contributions to the field through three key innovations: developing a fitness landscape rotation framework for dynamic benchmarking, introducing the concept of "elusivity" to quantify algorithmic adaptability over complete restart, and creating methods for generating realistic synthetic instances from real-world logistic data. Her work aims to bridge the gap between academic research and practical applications, particularly in sustainable logistics where optimization under uncertainty is critical. Dr. Alza Santos's publication record shows a consistent focus on dynamic optimization problems, particularly in combinatorial spaces. Her research trajectory demonstrates progression from theoretical foundations of dynamic permutation problems to increasingly practical applications, culminating in methods for generating realistic benchmarks from real-world data. A notable trend in her work is the development of metrics like "elusivity" that provide quantitative ways to evaluate the true adaptive challenge in dynamic optimization problems, moving beyond simplistic representations of dynamism. Her scientific recognition includes: Membership in the International Graduate Student Research Cohort (2024) Presentation at the Arctic Circle Assembly (2024) PhD defense with no corrections required (2025) - noted as a rare distinction As an early-career researcher, Dr. Alza Santos actively supervises research projects and serves as a Demonstrator for "Applied Data Science" and "Data Visualisation" modules. Her industrial collaborations span multiple sectors including energy, transportation, and environmental sustainability. She has worked on projects evaluating offshore wind farm impacts, developing visualization tools for fleet management, simulating transportation demand, optimizing cargo transportation, creating VR flooding simulations, reviewing childhood obesity data, and developing predictive maintenance platforms for the oil and gas industry. Dr. Alza Santos leads research within the Complex Optimisation Research Group at RGU, where she continues to develop innovative approaches to dynamic optimization problems. Her current work focuses on applying simulation-optimization techniques to address real-world challenges, particularly in sustainable logistics. She is actively seeking new collaboration opportunities with both academic researchers and industry partners to expand the impact of her work on society and the economy.

• Combinatorial Optimisation
• Explainable AI
• Metaheuristics
• +7 more

Dr. Lee Christie is a Research Fellow at Robert Gordon University's School of Computing, Engineering & Technology, where he conducts research in optimization and artificial intelligence. He is affiliated with the Complex Optimisation Research Group and maintains connections with the National Subsea Centre through his research on net-zero operations. His educational background includes: BSc (Hons) in Computer Science from Robert Gordon University (2003-2007) MSc in Information Engineering (Distinction) from Robert Gordon University (2009-2011, part-time) PhD in Computational Intelligence from Robert Gordon University (2011-2016) Dr. Christie's research primarily focuses on combinatorial optimization, structure learning, and blockchain technologies. He investigates how to make non-deterministic solvers more transparent through trajectory mining and feature extraction. His work bridges theoretical optimization techniques with practical applications in transportation systems (particularly connected autonomous vehicles), renewable energy (wind farm optimization), and supply chain management. He has published extensively on explainable metaheuristics, with a growing emphasis on making optimization algorithms interpretable for end-users while maintaining effectiveness. His recent publications (2021-2025) demonstrate a clear research trajectory toward explainable AI for optimization algorithms, with applications spanning transportation systems, renewable energy infrastructure, and complex supply chains. These works showcase his ability to translate theoretical advances into practical solutions for real-world problems with societal impact. Dr. Christie has secured research funding for projects including the A.R.T. Forum NSR (2019-2022), which developed implementation roadmaps for automated road transport in the North Sea Region. He teaches programming for business analytics courses and actively contributes to the Aberdeen Python User Group as a steering committee member. His academic service includes supervision of PhD students, with Dr. Martin Fyvie recently completing a dissertation on 'Explainability of Non-Deterministic Solvers' under his guidance as second supervisor. Dr. Christie maintains active collaborations with researchers including John McCall, A.-C. Zăvoianu, and A.E.I. Brownlee, resulting in consistent publication output across reputable venues in evolutionary computation and artificial intelligence.

• Evolutionary Computation
• Multi-Objective Optimization
• Artificial Intelligence
• +7 more

Dr. Ciprian Zavoianu is an academic researcher at Robert Gordon University (RGU) in the School of Computing, Engineering & Technology. He leads the Net Zero Operations research programme at the National Subsea Centre and is affiliated with the Complex Optimisation Research Group. His work focuses on applying artificial intelligence, particularly evolutionary computation algorithms, to solve complex real-world optimization problems with practical engineering applications. Dr. Zavoianu earned his academic qualifications from West University of Timisoara, Romania (BSc and MSc in Computer Science) and Johannes Kepler University Linz, Austria (PhD in Computer Science, 2015). His doctoral research focused on enhancing multi-objective evolutionary algorithms for computationally-intensive optimization problems. His primary research interests include: Evolutionary Computation Multi-Objective Optimization Data Mining & Machine Learning (particularly for surrogate modeling) Timetabling and Rostering Parallel/Distributed Computing Dr. Zavoianu's recent publications (2021-2025) demonstrate a strong focus on applying optimization techniques to transportation systems, electrical machine design, and sustainable energy solutions. His work consistently addresses the challenge of computationally expensive optimization through innovative surrogate modeling approaches, enabling practical applications of evolutionary algorithms to real-world engineering problems. He has secured multiple research grants including 'Data For Net Zero' and 'Ferry Passenger and Freight Modelling for Shetland,' demonstrating the practical relevance and industry applicability of his research. Dr. Zavoianu actively supervises five PhD/EngD students across diverse topics including predictive analytics for subsea installations, optimization of electrical machines, and operations optimization for harbor operations. His supervision approach emphasizes bridging theoretical algorithm development with practical implementation in engineering contexts. His laboratory work is centered around the National Subsea Centre, where he leads the Net Zero Operations research programme, focusing on sustainable solutions for the energy transition through advanced computational methods.

Khaled MESGHOUNIView profile

Lecturer

• Optimization
• Transportation Systems
• Railway Operations
• +3 more

Khaled MESGHOUNI serves as a Lecturer at Centrale Lille, affiliated with the OSL (Optimisation et Sûreté des Systèmes) research team within the CRIStAL laboratory. His office is located in C313 at the Scientific City campus. His research centers on optimization and system safety in transportation infrastructure, with specific expertise in railway operations, autonomous vehicle systems, and port logistics. He employs computational intelligence methods including genetic algorithm hybridization to solve complex scheduling and routing challenges in real-world mobility networks. Dr. MESGHOUNI has supervised multiple doctoral candidates through CRIStAL, including Quoc Khanh Dang's work on train routing in railway stations (2021), Zhanjun Wang's research on shared vehicle management systems (2015), and Radhia Zaghdoud's thesis on autonomous vehicle applications for port infrastructure (2015). His OSL team focuses on developing intelligent solutions for transportation system reliability and efficiency.

• Artificial intelligence in cyber/information Security
• IoT/IIoT vulnerability analysis and security
• Zero-trust architecture for IIoT
• +7 more

Dr. Sadaf Hina is a Lecturer in Cybersecurity at the School of Science, Engineering and Environment (SSEE), University of Salford, Manchester, UK. She serves as the Programme Leader for the BSc Computer Science with Cybersecurity and holds significant research leadership roles as REF 2029 Co-Lead for People, Culture and Environment. Dr. Hina is an active member of the IoT Research and Innovation Lab (IRIL) and contributes to the Informatics Research Centre at the university. Her research interests span the critical intersection of artificial intelligence and cybersecurity, with specific expertise in IoT/IIoT security, zero-trust architectures, threat modeling in cyber-physical systems, and AI-aided security optimization. Over the past five years, her publication record shows consistent output in high-impact journals, with a notable increase in 2023-2025 focusing on cutting-edge applications of deep learning and vision transformers for security challenges. Her work demonstrates strong connections between theoretical security frameworks and practical industrial applications, particularly in critical infrastructure sectors. Dr. Hina has received significant professional recognition including: Fellow of the Higher Education Academy (FHEA, 2024) Advance HE Aurora Leadership Development Program certification (2025) REF Co-Lead roles for both People, Culture and Environment (2025) and Contributions to Knowledge and Understanding (2023) Editorial position for Applied Sciences journal (2024) Membership in professional organizations including IEEE (since 2014) and British Computer Society As a dedicated educator, Dr. Hina teaches Information Security, Information Security Management, Network Penetration Testing, Security and Privacy in IoT, and Fundamentals of Cybersecurity. She actively supervises BSc final year projects, MSc dissertations, and three PhD candidates working on AI-aided cybersecurity solutions. Her leadership extends to serving as Faculty Advisor for Women in Cybersecurity (WiCyS) and participation in cross-institutional mentoring programs, demonstrating commitment to developing the next generation of cybersecurity professionals.

• Benefits Realisation in Project Management
• Value Generation in Projects
• Lean Manufacturing and Process Optimisation
• +3 more

Dr. Stylianos Sapountzis serves as Programme Leader for the MSc in Project Management at Salford Business School, University of Salford, where he leads modules including Project Management Leadership and Skills (PG), Advanced Project and Logistic Management (PG), and Project Management (UG). With an industrial background in manufacturing management within the electronics sector, he brings practical expertise to his academic role, having previously worked as a production manager and process engineer focusing on production optimization and new product introduction. His educational background includes: PG Cert Academic Practice (2015, University of Salford) PhD in Project Management (2013, University of Salford) PgDip Advanced Manufacturing Systems (1998, University of Salford) BSc (Hons) Manufacturing with Languages (1997, Manchester Metropolitan University) Dr. Sapountzis specializes in benefits realization frameworks, particularly his BeReal process for healthcare infrastructure projects, with research spanning value generation in projects, lean manufacturing applications, and digital transformation in project management. His scholarly work demonstrates a clear trajectory from healthcare infrastructure research toward contemporary applications of AI in project management and supply chain optimization, with recent publications examining digital transformation challenges in UK higher education and AI applications in negotiation activities. His research portfolio shows strong interdisciplinary connections between traditional project management principles and emerging digital technologies, with significant contributions to understanding how lean thinking can be applied across diverse sectors including healthcare, construction, and pharmaceutical manufacturing. The publication record reveals consistent output with increasing focus on digital transformation and AI applications since 2020. As a key member of the Health and Care Infrastructure Research and Innovation Centre (HaCIRIC), Dr. Sapountzis has presented research to international audiences and national bodies including the Department of Health, NHS Trusts, and Manchester City Council. His teaching expertise encompasses project planning and delivery tools, stakeholder management, and the Lean Toolbox, bridging theoretical concepts with practical industry applications developed through his earlier manufacturing career.

Anastasios KouvelasView profile

Lecturer

• Traffic flow theory
• System and control theory
• Optimization
• +10 more

Dr. Anastasios Kouvelas is a Lecturer at ETH Zurich, where he serves as head of the Road Traffic Engineering research group at the Institute of Transport Planning and Systems (IVT), Department of Civil, Environmental and Geomatic Engineering. He has held this position since August 2018, succeeding Dr. Monica Menendez who moved to New York University in Abu Dhabi. Prior to joining ETH Zurich, he was a research associate at the Urban Transport Systems Laboratory (LUTS) at EPFL (2014-2018) and a postdoctoral fellow at Partners for Advanced Transportation Technology (PATH) at the University of California, Berkeley (2012-2014). Dr. Kouvelas' research focuses on modeling, simulation, optimization and traffic flow control. His work aims to develop real-time solutions based on control theory and operations research methods. The Road Traffic Engineering group develops algorithmic solutions that are components of intelligent transportation systems used in traffic control centers. Recent technological advances in autonomous vehicles have expanded their research topics as the industry seeks efficient operational solutions for autonomous mobility. They are particularly interested in extending their work to the design of advanced management strategies for urban networks that utilize connected vehicles to improve traffic operations and develop network-wide control strategies that minimize environmental impacts. His recent publications (2023-2025) demonstrate strong focus on traffic prediction using deep learning techniques, bike lane allocation impacts on urban networks, transit network resilience against disruptions, vehicle trajectory extraction from aerial recordings, and traffic control for mixed traffic systems with connected and autonomous vehicles. His work bridges theoretical developments in control theory with practical traffic engineering challenges. Scientific Awards No specific scientific awards were mentioned in the provided information. Advising and Grants Dr. Kouvelas supervises PhD and Master's students in traffic engineering and intelligent transportation systems. His research is supported by various grants including a grant from the Hong Kong Research Grant Council (Grant No. GRF 11216323) for research on traffic speed prediction. Laboratories and Teams Dr. Kouvelas leads the multidisciplinary Road Traffic Engineering research group at IVT, which consists of researchers with backgrounds in civil engineering, electrical engineering, mechanical engineering, computer science, control, and operations research. The group's work spans multiple areas including traffic flow theory, traffic operations, connected and automated vehicles, and intelligent transportation systems.

Darren CoughlanView profile

Lecturer

• Sports Management
• Leadership Development
• Strategic Management
• +2 more

Darren Coughlan serves as Lecturer and Discipline Lead (Sports) at the International College of Management, Sydney (ICMS), leveraging over two decades of executive and consultancy experience in high-performance environments. His role bridges academic theory with real-world sports industry applications through innovative curriculum design and strategic industry partnerships. His educational credentials include postgraduate qualifications in Business Administration, Exercise Science, and Leadership, with ongoing completion of a Doctorate in Business Administration focused on strategic organisational transformation. Darren's research spans leadership development, strategic management, and human performance optimisation within sports contexts, establishing him as a thought leader in sports governance. His work prioritises practical methodologies that drive participation growth, revenue generation, and operational excellence in sports organisations through evidence-based human-centred approaches. While specific advising details remain unreported, his academic practice emphasises industry-integrated learning experiences. Strategic partnerships form the cornerstone of his approach to preparing sports management professionals, ensuring curriculum relevance through contemporary teaching methodologies and direct sector engagement.

Renatas KizysView profile

Associate Professor

• Asset pricing
• Cryptocurrencies
• Energy economics and finance
• +6 more

Renatas Kizys serves as Associate Professor in Finance and Deputy Head (Research) of the Department of Banking and Finance within Southampton Business School at the University of Southampton. His academic leadership spans research strategy and doctoral supervision in quantitative finance disciplines. His educational background includes: BA and MA in Economics from University of Vilnius, Lithuania MSc and PhD in Economics from University of Alicante, Spain Advanced Studies Program in International Economic Policy Research at Kiel Institute for World Economy, Germany Dr. Kizys' research program integrates financial theory with real-world market phenomena, focusing on asset pricing anomalies, cryptocurrency valuation mechanisms, energy finance dynamics, and environmental risk quantification. His work examines how macroeconomic variables, climate factors, and corporate governance structures influence financial markets and investment outcomes, with particular emphasis on portfolio optimization under uncertainty. Recent publications (2022-2024) reveal strong interdisciplinary convergence between finance, environmental science, and policy studies. Key trends include climate variables' impact on sovereign bonds, energy price cycles in M&A activity, corporate board diversity effects on environmental performance, and pandemic-related market volatility patterns across sectors. Scientific recognition includes: P.K. Woolley Research Fellowship at University of York He actively mentors PhD candidates with successful placements at Durham University, University of Portsmouth, University of Sussex, Yorkshire Building Society, Tianjin University of Commerce, and Africa Centre for Energy Policy. His teaching portfolio includes Quantitative Research in Finance (MANG6008) and Introduction to Finance (MANG6295) at postgraduate level. Dr. Kizys contributes to the Centre for Digital Finance and Centre for Empirical Research in Finance and Banking, collaborating on projects at the intersection of computational methods and financial market analysis.

Máté HegyhátiView profile

Associate Professor

• Supply Chain Management
• Petroleum Industry Optimization
• Logistics Engineering
• +3 more

Máté Hegyháti serves as an Associate Professor at the University of Pannonia's Faculty of Information Technology in Veszprém, Hungary. His academic work is closely tied to the PIMS Academy postgraduate program which focuses on petroleum industry supply chain optimization. His research interests center around supply chain management in the petroleum sector, with emphasis on optimization techniques for oil industry value chains. His work integrates mathematical modeling, downstream operations, and petrochemical business fundamentals to enhance efficiency across refining, logistics, and commercial operations. As part of the PIMS Academy initiative, Professor Hegyháti contributes to educating specialists capable of modeling and optimizing the entire petroleum value chain using industry-leading tools like Aspen Tech's optimization software. The program provides the accredited professional degree 'Specialist of Supply Chain Optimisation in Petroleum Industry (SCOPI)' through a practice-oriented, full-time postgraduate curriculum taught in English. His teaching encompasses critical areas including LP Modeling, Petroleum Refining, Petrochemical Industry operations, Project Evaluation, and Short Term Scheduling. The program maintains strong industry connections with MOL Group and other industrial partners to ensure practical relevance of the education provided.