3 PhD Candidates in Generative AI for Industrial Design Engineering (GenAIDE) Project

The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for:

3 PhD Candidates in Generative AI for Industrial Design Engineering (GenAIDE) Project 

Why apply?
Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda Research Institute Europe, Altair, MIT and 12 other partners to build an AI Partner in Engineering (AIPE) that converses with engineers, proposes designs, explains its reasoning and even writes new optimisation algorithms to optimize the designs.
We now hire three PhD candidates who be based at LIACS (Leiden University) and spend several months with industry and academic partners abroad.

The GenAIDE project 
GenAIDE (Generative AI for Industrial Design Engineering) is a 48-month MSCA Industrial Doctorate that wants to turn today’s computer-aided engineering tools into proactive team-mates.
The network of 16 academic & industrial partners will train 14 PhD fellows (3 of which are to be hired through this call). 

What GenAIDE offers you: 

• A structured, four-year PhD training programme that mixes:
  
  • personalised PhD training paths defined in your Career-Development Plan and backed by the Graduate Schools at each host (Leiden University). 
  • international industrial & academic secondments (3–6 months) giving hands-on experience at Altair, Honda Research Institute Europe, MIT, Samaya, MathWorks and others.
  • network-wide events – several week-long “Science & Skills” summer-schools/workshops that rotate between partners and blend theory, hackathons and career events.
• Dedicated supervision team (academic & industrial) 
• ECTS-accredited courses, travel funding to top conferences and continual peer exchange with 13 fellow GenAIDE PhDs spread across Europe and beyond.

In short, GenAIDE is not just a PhD programme, it’s a complete, multi-sector doctoral network that equips you with cutting-edge technical depth and the leadership, entrepreneurial and ethical toolkit to shape the next generation of human-AI design teams.

The 3 Doctoral Candidate positions:

1 – Enhancing Search Parameter–Output Relationships of Generative Shape Optimisation
This project focuses on embedding state-of-the-art LLMs into the set-up stage of generative shape-optimisation algorithms. The aim is to learn more expressive design-variable encodings and establish tighter, more intuitive links between search parameters and the resulting geometry or performance outcomes. The result will be an LLM-driven framework giving designers clearer cause-effect control over generative shape optimisation. 

2 – Explainable AI with LLMs for Transparent Decision-Making in Design Engineering
This project combines classic XAI methods with LLM capabilities to generate human-readable explanations during preference-based optimisation. The goal is to improve transparency and trust during collaborative human–AI design sessions. Outcomes include a new LLM-aided XAI toolbox, validated on both academic and industrial design cases, and guidelines for transparent human–AI co-creation. 

3 – Automated Algorithm Design using Large Language Models
This project applies LLMs to synthesise or fine-tune optimisation algorithms on demand from modular building blocks, guided by textual task descriptions and engineer preferences. The goal is to create a “LLM-AutoML” framework that produces tailored multi-objective evolutionary algorithms for shape-optimisation problems.

Key responsibilities 

• Assist in relevant teaching activities;
• Perform PhD research on one of the above topics and publish in top venues;
• Collaborate closely with the other GenAIDE fellows and industrial partners; complete planned secondments;
• Contribute to the NACO research cluster (naco.liacs.nl);     

Selection Criteria 

• MSc (or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field;
• Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C++);
• Proven interest in generative models, XAI, optimisation or CAD is a plus;
• Excellent command of English; willingness to work in an international, multi-sector environment;
• MSCA eligibility: candidates must not have r