PhD Researcher in Digital Process Twins

About the role

Four-year fully funded PhD at KU Leuven on digital process twins for industrial manufacturing. The work fuses sensor data, simulation, and machine learning to build real-time twins of complex process plants (chemical, food, pharma). Distinct from the demanufacturing PhD also open at KU Leuven, which focuses on end-of-life disassembly. Deadline 26 June 2026.

Responsibilities

• Build digital twin models of continuous and batch process plants.
• Integrate physical sensor streams with mechanistic simulation and ML surrogates.
• Validate twins against operational data from industrial partners and publish results.
• Publish in Journal of Process Control, AIChE Journal, Computers and Chemical Engineering, and IFAC venues.

Requirements

• Master’s degree in chemical engineering, mechanical engineering, control engineering, or computer science with relevant focus.
• Programming proficiency in Python and at least one simulation environment (Aspen, gPROMS, OpenModelica, or similar).
• Solid grounding in dynamic systems or process control.
• Fluent English.

Nice to have

• Industrial internship or applied research experience in process industries.
• Familiarity with model predictive control, system identification, or scientific ML.
• A first-author paper or strong Master’s thesis project.

How to apply

Open the apply link and submit CV, transcripts, motivation letter, Master’s thesis, and two reference contacts via KU Leuven’s job portal before 26 June 2026.