We seek for two postdoctoral researchers to join the Data and AI
https://dai.win.tue.nl/ and Software Engineering and Technology
https://set.win.tue.nl/ clusters for a two-year project on use of LLMs in software development at ASML, a world-leading supplier to the semiconductor industry. One position will focus on foundations and development of domain and task specific LLMs and their rigorous evaluation. The other position will focus on the application of generative AI techniques in the domain of software engineering, particularly, when it comes to software architecture support and technical debt reduction. It is expected that the postdocs will closely collaborate working together on use cases and will spend about half time at TU/e and half time at ASML (situated also in Eindhoven). Working from home one day a week is allowed.
The postdoctoral researcher joining the Data and AI cluster will work primarily on developing a task-specific LLM evaluation (including self-audit and performance profiling) and task-specific LMM fine-tuning frameworks. The postdoctoral researcher joining the Software Engineering and Technology cluster will focus on applying generative AI methods to software architecture and technical debt use cases. The work produced by both researchers should be framed in the context of real ASML use cases of software (re)engineering projects.
On the AI side, there will be immediate opportunities for collaboration with research group leaders: Mykola Pechenizkiy (Safe AI), Cassio de Campos (Uncertainty in AI), George Fletcher (Graph data management), Joaquin Vanschoren (autoML), and Jakub Tomczak (Generative AI) and their teams within the Data and AI cluster, known for studying foundational issues of AI robustness, reliability, tractability, scalability, interpretability and explainability.
On the Software Engineering research side, your main point of contact will be Lina Ochoa Venegas and Mark van den Brand (both from Software Engineering and Technology). There will be opportunities to work with other researchers like Michel Chaudron (software architecture), Alexander Serebrenik (human aspects in software engineering), and Anton Wijs (parallel software development) within the cluster when analysing the use cases.
On the ASML side, you will collaborate with Alok Lele and use case owners from the IT. The initial use cases being considered are:
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Technical debt: In the software engineering practice, the generation of added value to customers guides the implementation and maintenance roadmaps. When certain tasks are put on hold to address other urgent matters, technical debt emerges. Technical debt is a liability that results in acquired costs due to choosing a limited approach instead of a robust solution. We aim at identifying and resolving a part of a company's technical debt by using generative AI to refactor the compromised parts of the system.
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Architecture conformance check: The architecture of a system evolves as its implementation does. Verifying that the main design decisions and principles are still being fulfilled in the system is crucial to favor relevant quality attributes. We aim to use the power of Generative AI to identify higher-level components from source code and evaluate if and how the architecture of a system has drifted away from the allowed guidelines.