Organisation/Company: KU Leuven
Research Field: Engineering » Chemical engineering
Researcher Profile: First Stage Researcher (R1)
Country: Belgium
Application Deadline: 30 Apr 2025 - 00:00 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 38 hours/week
Offer Starting Date: 1 Sep 2025
Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA
Reference Number: BAP-2025-177
Marie Curie Grant Agreement Number: 101169530
Is the Job related to staff position within a Research Infrastructure? No
Offer Description
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:
1. Experimental synthesis and characterization of novel Z-scheme nano-photocatalysts.
2. Density Functional Theory (DFT) calculations to model photocatalyst reactivity and to use as quantum descriptors in the machine learning (ML) models.
3. ML-based predictive modeling, to relate photocatalyst properties with degradation performance.
4. 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.
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.
Eligibility criteria
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.
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