Are you curious how Deep Learning and Online Learning can be effectively combined to create new learning paradigms?
Job description
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 these are available, and also to learn from real-time, potentially non-IID, streaming data; should be able to track the evolution of key features and achieve model plasticity while avoiding catastrophic forgetting; and should 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, and Cyprus Institute).
Links & References:
https://faculty.cc.gatech.edu/~dovrolis/
https://www.cyi.ac.cy/index.php/castorc/about-the-center/castorc-our-people/author/1295-constantine-dovrolis.html
https://www.tudelft.nl/en/eemcs/the-faculty/departments/software-technology/networked-systems
- N. Mhaisen, G. Iosifidis, On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning, ICML, 2025.
- G. Iosifidis, N. Mhaisen, D. Leith, Optimistic Learning for Communication Systems, available in Arxiv, 2026.
- Cameron Ethan Taylor, Shreyas Malakarjun Patil, Constantine Dovrolis: Before Forgetting, There's Learning: Representation Learning Challenges in Online Unsupervised Continual Learning. Trans. Mach. Learn. Res. 2025 (2025)
- Mustafa Burak Gurbuz, Xingyu Zheng, Constantine Dovrolis: PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity. ICML 2025
Job requirements
- 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.
- Mathematical Foundations: Knowledge of optimization techniques (e.g., LP, CVX, etc), including for/with ML (first order methods, data-driven algorithms, etc).
- Data Foundations: Hands on experience in data analysis (Python, etc.), experience in evaluation of algorithms and in 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. Reflecting on your motivation for pursuing a PhD trajectory is essential, as this path involves unique challenges and uncertainties inherent to scientific exploration. Success requires dedication, adaptability, the ability to analyze complex problems, manage your 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. These experiences will build you as an independent researcher, expand your professional network, and pave the way for diverse career pave the way for diverse career paths, inside or outside academia.
TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.
At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.
Challenge. Change. Impact!
Faculty of Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.
Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.
Conditions of employment
Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 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 everything goes well and performance requirements are met.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3059 - €3881 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.
As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.
The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.
Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.
Additional information
If you would like more information about this vacancy or the selection procedure, please contact Georgios Iosifidis, via G.Iosifidis@tudelft.nl.
Application procedure
Are you interested in this vacancy? Please apply no later than 31 May 2026 via the application button and upload the following documents:
- Detailed CV
- Motivation letter (maximum 1 page)
- BSc and MSc Transcripts (list of courses and grades)
You can address your application to Georgios Iosifidis. This PhD position is part of the Marie Curie Doctoral Network FINALITY. The succesful candidate will join an academic-industrial consortium working on the foundations and applications of Safe Learning, and will be co-supervised by two advisors (Prof. Dovrolis, Prof. Iosifidis) at TU Delft, with secondments at University of Cyprus and other partners.
Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.
Please note:
- You can apply online. We will not process applications sent by email and/or post.
- As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
- Please do not contact us for unsolicited services.
