PhD position: Nano-photocatalysts for degradation of CECs using machine learning

The current position will be embedded in the CREaS division of the Chemical Engineering Department at KU Leuven. The team lead by Prof. Raf Dewil will be the main host and focuses on innovative wastewater treatment, advanced oxidation processes, nanomaterial-based water purification, and resource recovery. We develop sustainable, high-performance chemical and electrochemical treatment technologies, with applications in contaminant removal, circular economy strategies, and industrial process optimization. Using advanced spectroscopy, chromatography, and pilot-scale testing, we aim to bridge the gap between fundamental research and real-world environmental challenges. We collaborate with industrial partners and international institutions to develop scalable, energy-efficient solutions for wastewater treatment and pollutant degradation. To cover artificial intelligence (AI) aspects included in this PhD topic, the work will be co-supervised by Prof. Florence Vermeire, who specializes in machine learning-driven process optimization and data analysis. Her expertise ensures that modeling and computational techniques complement the experimental work in nanophotocatalysis, leading to optimized performance and predictive modeling of treatment processes.
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Project

This PhD position is part of NANAQUA, a Marie Skłodowska-Curie Doctoral Network (MSCA-DN) focused on developing innovative, sustainable, and energy-efficient nanophotocatalytic processes for wastewater treatment. The project aims to synergistically integrate machine learning with nanophotocatalysis to enhance contaminant mineralization and solar-driven water purification.

The doctoral candidate will develop Z-scheme nano-photocatalysts, optimizing their light absorption, charge transfer, and surface reactions for the efficient degradation of contaminants of emerging concern (CECs) under visible-light irradiation. A machine learning-driven approach will be used to predict and optimize photocatalyst performance by analyzing key parameters such as photocatalyst dose, pH, illumination time, and pollutant concentration.

The research will involve (i) experimental synthesis and characterization of novel Z-scheme nano-photocatalysts, (ii) Density Functional Theory (DFT) calculations to model photocatalyst reactivity and to use as quantum descriptors in the machine learning (ML) models, (iii) ML-based predictive modeling, to relate photocatalyst properties with degradation performance and (iv) experimental validation and kinetic modeling to enhance reaction efficiency and mechanistic understanding of CEC degradation.

Expected Results:

• Development of next-generation nano-photocatalysts for sustainable water treatment.

• New insights into the degradation mechanisms of organic pollutants.

• ML-based predictive model for optimizing photocatalytic performance and scaling up real-world applications.

The experimental aspects of this project will be supervised by Prof. Raf Dewil, while Prof. Florence Vermeire will lead the ML-driven optimization. This interdisciplinary research offers a unique opportunity to work at the intersection of nanomaterials, photochemistry, and artificial intelligence, contributing to sustainable water purification solutions within an international research network.

Profile

We are looking for a highly motivated and ambitious candidate with a strong background in chemical engineering, environmental engineering, materials science, or a related field, combined with experience in nanomaterials, catalysis, and computational modeling. 

The ideal candidate should have a passion for experimental and computational research in sustainable wastewater treatment and environmental nanotechnology.

Qualifications & Skills:

-          A Master’s degree in Chemical Engineering, Environmental Engineering, Materials Science, or a related field.

-          Experience in catalysis and photocatalysis, with a solid understanding of wastewater treatment processes and advanced oxidation techniques.

-          Knowledge of photocatalytic heterojunction materials.

-          Interest in computational approaches and machine learning. Preliminary expertise with python or similar languages is required, expertise in advanced machine learning is an added value.

-          Proficiency in English, both written and spoken.

Candidates with experience in both experimental and computational research are strongly encouraged to apply. 

This position offers a unique opportunity to work at the intersection of nanomaterials, photochemistry, and artificial intelligence, contributing to the advancement of next-generation water treatment technologies. 

Offer

We offer a fully funded 3-year PhD position in a dynamic and interdisciplinary research environment. 

Our lab is committed to providing comprehensive support to ensure the successful completion of your PhD, offering high-quality scientific training in both experimental and computational research, and this within the NANAQUA consortium framework.

Interested?

For more information please contact Prof. dr. ir. Raf Dewil, tel.: +32 15 68 82 29, mail: [email protected] or Mrs. Ellen Verduyckt, tel.: +32 15 68 80 64, mail: [email protected].

KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.