Jobid=37ed4d31a8cf (0.0156)
Job description
Are you curious how Deep Learning and Online Learning can be effectively combined to create new learning paradigms?
Online learning algorithms achieve robustness often at the expense of performance, as they are very cautious by design. This, in turn, makes them less practical for problems where speed is of utmost priority. On the other hand, offline learning, such as Deep Learning, often suffers from distribution shifts, lack of training data, and poor adaptability to unseen conditions and new problems. Can we combine these two fundamental learning paradigms to synthesize new learning tools that are both fast and adaptive?
This thesis aims to develop a robust hybrid learning framework that lies at the nexus of online and offline learning. The developed algorithms should be able to benefit from training data when available and also learn from real‑time, potentially non‑IID, streaming data; track the evolution of key features and achieve model plasticity while avoiding catastrophic forgetting; and come with interpretable and robust accuracy (generally performance) guarantees.
The designed algorithms will be applied to key problems in the domain of safe learning for interconnected systems (e.g., 6G and Edge AI platforms, self‑driving vehicle vision) in collaboration with industry partners and domain experts.
This PhD thesis is offered in the context of the Marie Curie Doctoral Networks “FINALITY”, will be hosted at TU Delft, Department of Computer Science, and will be co‑supervised by Prof. George Iosifidis (TU Delft) and Prof. Constantine Dovrolis (University of Cyprus, Cyprus Institute).
Qualifications
- Master’s degree in Computer Science, Machine Learning, Operations Research, Applied Mathematics, or related fields.
- Bachelor degree in Mathematics, Data Science, Computer Science, Electrical Engineering, Operations Research, or related fields.
- Knowledge of optimisation techniques (e.g., LP, CVX, etc.), including first‑order methods for machine learning.
- Hands‑on experience in data analysis (Python, etc.), evaluation of algorithms and Deep Learning libraries.
- Excellent command of written and spoken English (subject to TU Delft eligibility criteria).
To thrive as a PhD candidate, it’s crucial to have a strong research mindset driven by curiosity and passion for your topic. Success requires dedication, adaptability, the ability to analyse complex problems, manage time effectively, innovate and stay resilient under pressure. Combined with the ability and willingness to work independently and collaborate well, these qualities are indispensable for a fulfilling PhD journey.
Conditions of employment
Doctoral candidates will be offered a 4‑year period of employment in principle, but in the form of two employment contracts: an initial 1.5‑year contract with an official go/no‑go progress assessment within 15 months, followed by an additional contract for the remaining 2.5 years assuming satisfactory performance.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, ranging from €3,059 to €3,881 gross per month (first year to fourth year) for a full‑time contract (38 hours), plus 8% holiday allowance and an end‑of‑year bonus of 8.3%. The PhD candidate will be enrolled in the TU Delft Graduate School and benefits from a customisable compensation package, discounts on health insurance, a monthly work‑costs contribution, and flexible work schedules.
Application procedure
- Detailed CV
- Motivation letter (maximum 1 page)
- BSc and MSc transcripts (list of courses and grades)
Are you interested in this vacancy? Please apply no later than 31 May 2026 via the application button and upload the above documents. You can address your application to Georgios Iosifidis. The successful candidate will join an academic‑industrial consortium working on the foundations and applications of Safe Learning and will be co‑supervised by Prof. Dovrolis and Prof. Iosifidis at TU Delft, with secondments at University of Cyprus and other partners.
Please contact Georgios Iosifidis via for further information.
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