Mumin Enis Leblebici

This fully funded PhD position at KU Leuven is part of the AQUAEra project, a transnational EU-funded initiative under the Water4All Partnership. The project aims to revolutionize urban wastewater treatment by integrating advanced oxidation processes (AOPs), photocatalysis, and renewable energy generation. KU Leuven leads the Modelling and Optimization work package, focusing on developing hybrid models that combine first-principle and machine learning approaches, creating predictive frameworks for photocatalytic degradation and hydrogen production, and designing multi-objective optimization algorithms to maximize both environmental and economic performance. As a PhD researcher, you will build and validate hybrid models for photocatalytic processes, apply machine learning to experimental datasets from international consortium partners, and develop and implement multi-objective optimization algorithms such as NSGA-II and MOPSO. You will collaborate closely with teams from FEUP, NTNU, CSIC, and NRC, contributing to publications, conferences, and the development of scalable solutions for sustainable water-energy systems. The Leblebici Team at KU Leuven is known for its interdisciplinary and collaborative approach, specializing in advanced modelling, simulation, and optimization techniques for chemical and environmental processes. Alumni have gone on to prestigious postdoctoral fellowships and elite positions in government, academia, and industry. The position offers access to state-of-the-art facilities, international collaboration, mobility opportunities, training, and participation in European workshops and schools, all within a dynamic and supportive research environment. Applicants should have a Master’s degree in Chemical Engineering, Process Engineering, Computer Science, or related fields, with a strong background in modelling, simulation, and/or machine learning. Experience with data preprocessing, algorithm development, and optimization techniques is required, as are excellent communication skills in English. Familiarity with Python, MATLAB, or similar tools is expected, and prior experience with environmental or photocatalytic systems is a plus. KU Leuven is committed to diversity, inclusion, and equal opportunity, providing a respectful and socially safe environment for all researchers. The application deadline is January 15, 2026. For more information, contact Prof. dr. Mumin Enis Leblebici at [email protected].

PhD opportunity at KU Leuven in automated feature discovery and data-driven modelling for chemical processes. The research group at KU Leuven works at the intersection of chemical engineering, process systems engineering, and artificial intelligence. The project is part of EL4CHEM – Efficient Learning for Chemical Applications and focuses on developing automated methods to identify the most informative features and build reliable data-driven models for complex chemical and pharmaceutical processes. The PhD researcher will study how to select and generate the right variables for modelling when process datasets contain many possible inputs, such as operating conditions, sensor measurements, material properties, molecular descriptors, and engineered process variables. Because experimental data are often scarce, noisy, and expensive, the project aims to make feature discovery and model identification more intelligent, robust, and interpretable. Research may include automated feature generation and feature selection, sparse and interpretable machine-learning models, nonlinear feature interactions, dimensionality reduction, automated comparison of model structures, incorporation of physical and chemical knowledge into selection workflows, uncertainty and robustness analysis, explainable AI, and development of automated modelling workflows for different industrial use cases. The methods will be tested on real applications from the chemical, pharmaceutical, and manufacturing sectors through the EL4CHEM consortium. The project is therefore well suited to a candidate who enjoys combining methodological AI research with practical industrial modelling challenges. Candidate profile: the advert seeks applicants with a background in chemical engineering, process engineering, applied mathematics, data science, computer science, or a related field. Python skills and prior experience with machine learning, statistical modelling, feature selection, optimisation, or process modelling are advantageous. A strong interest in applying machine learning to chemical-engineering problems is essential, together with motivation, independence, and enthusiasm for interdisciplinary research. Offer: a full-time research position for one year, with the possibility of extension up to four years depending on performance and available funding. The position offers an international and multidisciplinary environment, opportunities for publication, collaboration with industrial partners, and development of new data-driven modelling methodologies with direct industrial relevance. Application and contact: interested candidates are invited to apply via the KU Leuven job link. For more information, contact Prof. dr. Mumin Enis Leblebici at [email protected] . Location: Leuven, Belgium. Deadline: 2026-09-30.