PhD on automated feature discovery and data-driven modelling for chemical processes

KU Leuven; Vlaams-Brabant; Sur place; EUR 42 000 - 52 000; Plein temps

Avantages offerts par ce poste

Publication opportunities

Industrial collaboration

International team

Résumé du poste

KU Leuven invites applications for a PhD on automated feature discovery and data-driven modelling for chemical processes. The project is part of EL4CHEM and aims to develop automated feature generation, selection and model identification methods that yield accurate, interpretable models for industrial chemical processes.

The candidate will work at the interface of chemical engineering, AI, and process systems engineering, applying ML to real datasets with strong industrial relevance and

Qualifications

• PhD in chemical engineering, process engineering, applied mathematics, data science, computer science or a related field.
• Experience with Python and machine learning is advantageous.
• Strong interest in combining ML with chemical-engineering problems; motivated and interdisciplinary.

Connaissances

Python

Machine learning

Data science

Feature selection

Process modelling

Description du poste

PhD on automated feature discovery and data-driven modelling for chemical processes

Our group at KU Leuven works at the interface of chemical engineering, process systems engineering and artificial intelligence. We develop hybrid models, digital twins and data-driven tools for process design, optimisation and control, with a strong focus on industrially relevant applications.

Project

Project

Within EL4CHEM – Efficient Learning for Chemical Applications, the researcher will develop automated methods to identify the most informative features and construct reliable data-driven models for complex chemical and pharmaceutical processes.

Industrial process datasets typically contain many potential inputs, ranging from operating conditions and sensor measurements to material properties, molecular descriptors and engineered process variables. At the same time, experimental data are often scarce, noisy and expensive to obtain. Selecting the right information therefore becomes as important as selecting the modelling method itself.

The PhD will investigate automated feature generation, feature selection and model identification methods that can determine which variables and representations are most relevant for a given prediction or modelling task. The work will combine modern machine-learning techniques with chemical-engineering knowledge to develop models that are accurate, interpretable and robust under limited-data conditions.

Research topics may include:

• automated feature generation and selection for process and product data;
• sparse and interpretable machine-learning models;
• nonlinear feature interactions and dimensionality reduction;
• automated comparison and selection of data-driven model structures;
• incorporation of physical and chemical knowledge into feature-selection workflows;
• uncertainty and robustness of selected features and models;
• explainable AI methods to identify the physicochemical and process variables governing model predictions;
• development of automated modelling workflows that can be applied across different EL4CHEM industrial use cases.

The developed methods will be evaluated using real industrial applications from the chemical, pharmaceutical and manufacturing sectors within the EL4CHEM consortium.

Profile

We are looking for a candidate with a background in chemical engineering, process engineering, applied mathematics, data science, computer science or a related field.

Experience with Python, machine learning, statistical modelling, feature selection, optimisation or process modelling is an advantage.

A strong interest in combining machine learning with chemical-engineering problems is essential. The candidate should be motivated, independent and interested in interdisciplinary research involving both methodological development and industrial applications.

Offer

We offer a full-time research position for one year, with the possibility of extension up to four years, depending on performance and available funding.

You will work in an international and multidisciplinary environment at the intersection of chemical engineering and artificial intelligence, with opportunities for scientific publication, collaboration with industrial partners and development of new data-driven modelling methodologies with direct industrial relevance.

Interested?

For more information please contact Prof. dr. Mumin Enis Leblebici, mail: [e-mail].

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.

location_city Locatie: Leuven