Summary: The Eindhoven University of Technology (TU/e) is offering a fully funded, four-year PhD position in a unique interdisciplinary project. The candidate will work at the intersection of machine learning and formal methods to develop resilient AI systems that can detect failures, adapt, and recover, all while being backed by rigorous formal guarantees.
Overview
| Feature | Details |
|---|---|
| Institution | Eindhoven University of Technology (TU/e) |
| Department | Mathematics and Computer Science |
| Position Type | Full-time (1.0 FTE) |
| Duration | 4 years (intermediate assessment after 9 months) |
| Salary | €3,204 – €4,051 gross per month |
| Reference Number | 2026/443 |
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Subject Area: Computer Science, Mathematics, Artificial Intelligence, Machine Learning, and Formal Methods.
Location: Eindhoven University of Technology (TU/e), Brainport Eindhoven, Netherlands.
Eligibility: To qualify for this position, applicants must meet the following requirements:
- An MSc degree (or near completion) in computer science, mathematics, artificial intelligence, or a closely related discipline.
- A strong foundation in machine learning, including statistical learning theory and probabilistic models (knowledge of robustness or uncertainty quantification is a plus).
- Mathematical maturity and experience with formal or rigorous reasoning.
- Strong programming skills, preferably in Rust or Python (experience with PyTorch is preferred).
- Fluency in spoken and written English (at least C1 level).
- A highly motivated, creative, and self-driven working style with the ability to collaborate in an interdisciplinary team.
- Willingness to contribute to teaching tasks (10-15% of the employment).
Scholarship Description: While existing machine learning often focuses on robustness against noisy data, this PhD project aims to develop resilient machine learning models that can close the loop between failure and adaptation. The research will focus on creating methods that detect when a system fails, learn from these mispredictions, and recover to a stable state. Formal methods will be applied to ensure that this learning and recovery process comes with provable quality-of-service guarantees. The candidate will be co-supervised by experts from both the Data and AI cluster and the Formal System Analysis cluster, utilizing resources like the TU/e HPC cluster SPIKE-1.
How to Apply: Applications must be submitted online via the university’s application portal. Emailed or posted applications will not be processed. A complete application must include:
- Cover Letter: Maximum 2 pages describing your motivation and qualifications, including a brief statement on your experience with machine learning and/or formal methods.
- Transcripts: Official grade transcripts of both BSc and MSc education.
- Curriculum Vitae (CV): Including a list of projects, publications, and other relevant items.
- MSc Thesis: A copy of or a link to your MSc thesis (or an academic writing sample if the thesis cannot be shared).
Note: A pre-employment screening, such as a knowledge security check, may be part of the selection procedure.
Last Date to Apply: August 20, 2026
