The speeches of the 2025 Doctoral Open Day at ELTE Faculty of Informatics

This event was designed for MSc students who are curious about pursuing a PhD and want to explore the exciting opportunities that come with it. You’d hear inspiring elevator talks from current and former PhD students, sharing their experiences, research journeys, and the incredible impact of doctoral studies on their careers. In addition, you could learn everything about applying for doctoral training and available scholarships.

Intro

Prof. Horváth Zoltán, Head of Doctoral School

From Curiosity to Discovery: Why a PhD Journey is Worth It?

PhD studies offer a unique opportunity to engage in advanced research and contribute to scientific knowledge. At ELTE Faculty of Informatics, students are encouraged to begin research activities already during their master’s studies. This early engagement provides a solid foundation for future doctoral work.

Recent regulatory changes allow students to start their PhD before completing their master’s degree, enabling them to pursue both simultaneously. In this integrated path, some credits can be counted towards both programs, creating an efficient academic progression. However, students are advised to define a clear research question, secure an experienced supervisor, and join a research group before starting the PhD journey.

PhD Structure and Requirements
The doctoral program consists of three main phases:

1. Training and Research Phase (Semesters 1–4):
   Students complete coursework and begin focused research. After earning 120 credits, they must pass a complex examination to continue.
2. Research and Dissertation Phase (Semesters 5–8):
   The emphasis shifts entirely to research, publication of results, and dissertation writing. A further 120 credits are required during this phase.
3. Dissertation Submission:
   Students must submit their dissertation within three academic years of passing the complex exam. Before the public defense, an internal disputation is organized to provide feedback from reviewers.

Recommendations for Success
Begin research as early as possible, ideally during your MSc studies.

• Develop a well-structured research plan prior to starting the PhD.
• Regularly publish in peer-reviewed journals to validate progress.
• Choose a project-based topic with real-world relevance and, if possible, industrial collaboration.
• Participate in the Cooperative Doctoral Program (KDP) or the University Excellence Program (EKÖP) to access additional funding and professional support.

PhD students at ELTE often supervise MSc students, lead research groups, and collaborate with industry partners. This prepares them for academic or R&D careers, while also contributing to innovation and practical applications.

Funding Opportunities
The basic monthly doctoral fellowship is 140,000 HUF. Students engaged in cooperative or excellence programs may be eligible for additional funding.

Conclusion
Doctoral research is a path to solving real-world problems with scientific rigor. At ELTE, students are supported in contributing to impactful, often open-source research that benefits society as a whole.

Student’s experience

Tófalvi Tamás

Not Just a Student

I am currently a third-year PhD student at ELTE, part of the Geometric Computer Vision group. My PhD research is being carried out in collaboration with Bosch and ELTE.

My main research focuses on computer vision, specifically the use of remote sensing sensors such as LiDARs. My work addresses topics like data fusion, sensor calibration, and related technological challenges.

A PhD is not simply a continuation of student life, but rather an entry-level position in the journey toward becoming a researcher or building a career in academia. This period marks the beginning of a professional research career, where one takes the lead on independent research and actively contributes to the scientific community.

While a PhD may not be the most financially rewarding decision in the short term, it offers substantial long-term value. The most significant benefit, in my view, is the freedom it provides to conduct your own research, pursue your curiosity, and explore new scientific directions—freedom that is rare in many other professional settings.

Moreover, a PhD is not only about research; it also provides opportunities to teach. I currently teach two courses, and this experience has greatly enriched my academic career. Teaching allows me to share my knowledge and engage with students, fostering both personal and professional growth.

In summary, a PhD offers the chance to develop not only as a researcher but also as an educator. This combination of research and teaching can be incredibly rewarding and provides valuable experience for anyone considering an academic career.

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Kolarovszki Zoltán

Classical Tools, Quantum Questions: My PhD Experience

My PhD research focuses on quantum computing, specifically the development of high-performance photonic quantum computer simulators. I am currently involved in the Piquasso project, which is a simulator designed to help design circuits and test ideas for photonic quantum computers. Since photonic quantum computers are not yet widely available, simulators like Piquasso are essential for advancing this field. The Piquasso project is open-source and available on GitHub for those interested.

My academic journey started in a different field. Initially, I studied chemical engineering, but I later transitioned to physics, and eventually, I found my way into computer science. This transition was not as drastic as it may seem, as my current work in quantum computing is deeply rooted in physics. Along the way, I also worked in various roles, including as a process engineer and software developer. Currently, I am a junior research fellow at the Wigner Research Center for Physics, which is closely aligned with my PhD research.

The key research questions I am addressing in my PhD include exploring the practical applications of photonic quantum computers, an open question in the field. Additionally, I am investigating challenges related to photonic quantum machine learning, which merges quantum computing and machine learning. Another area of interest is whether photonic quantum algorithms are difficult to simulate on classical computers. I am also working on implementing randomized quantum circuits, a complex problem within quantum computing.

In addition to my research, I have been involved in teaching. Initially, I taught imperative programming, but I found that this did not align with my interests, so I transitioned to teaching a course on the basics of quantum computing at ELTE. Through this course, I have had the opportunity to mentor several students, two of whom have already defended their theses.

Throughout my PhD, I have had the opportunity to travel and attend conferences, presenting my work at international events. This experience has not only been enriching for my research but also an excellent way to engage with the global scientific community.

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Itilekha Podder

Bridging Academia and Industry: A PhD Journey in Explainable AI for Smart Manufacturing

I am currently in the final year of my PhD program, during which I have been conducting research in collaboration with Bosch as an academic researcher. My work has been made possible through EIT, and I am grateful for the opportunity to engage in a project that bridges academia and industry.

My research focuses on implementing AI-based solutions, with a particular emphasis on explainable AI, to improve semiconductor manufacturing processes. This work is directly connected to my position at Bosch, where I am involved in projects aimed at improving production lines. The core goal of explainable AI is to make AI decision-making processes transparent and interpretable, allowing engineers to trust and apply these solutions in real-world manufacturing environments.

In some cases, complex hybrid deep learning models are not suitable for real-time applications. In these situations, more subtle and simpler solutions are required, though implementing them in real time can present significant challenges. Throughout my research, I have explored both types of solutions, gaining valuable insights into their respective advantages and limitations.

The academic component of this research is crucial, as it provides the deep theoretical understanding necessary to determine which solutions are most appropriate for specific contexts. The industrial aspect is equally important, as it allows the translation of theoretical solutions into practical implementations. The real-world data and use cases encountered in industry, while sometimes challenging, make the process of applying these solutions highly rewarding.

Some of the specific use cases I have worked on include predictive maintenance, sensor calibration, real-time analytics, and developing frameworks for industrial applications. This work represents a perfect intersection of theory and practice, with tangible impacts on the manufacturing process.

Throughout this journey, several challenges have emerged, with one of the key questions being which technology to use. Should deep learning, machine learning, or another approach be applied? While there are subtle differences between these methods, the most important factor is understanding how to select the appropriate solution for the given problem. This ability comes from the academic training and knowledge gained during the PhD process.

Persistence is another crucial element of the PhD journey. Success is not always about immediate excellence, but rather about maintaining consistency and not giving up when challenges arise. Often, a solution that initially seems ideal may turn out to be unsuitable for the problem at hand, and it is through persistence that better solutions are found.

Work-life balance is also an important consideration. While there will inevitably be times when weekends are devoted to research, it is important to maintain a balance for long-term well-being and productivity.

The key takeaways from my experience include the invaluable nature of industrial collaboration, which allows for the application of research in real-world contexts. Additionally, effective communication is essential. Initially, I lacked confidence in public speaking, but over time, I have learned the importance of adjusting how results are communicated depending on the audience. This skill has been a key aspect of my personal and professional development throughout the PhD process.

I have also had the privilege of working with exceptional individuals, including students whom I have mentored. This journey has been an incredible learning experience, and I am grateful for every part of it.

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Horváth Dániel

PhD without Borders: Research and Collaboration between ELTE, SZTAKI, and Mines Paris

I recently completed my PhD, with my public defense taking place just a month ago. This section shares insights from my experience with international research collaborations, particularly across multiple institutions. My aim is to provide an overview that might inspire others to pursue a PhD.

My research focused on robotics, particularly the intersection of robotics and deep learning. The main topics of my work included developing methods to transfer knowledge from simulation to real-life applications and designing specialized curricula for reinforcement learning algorithms to enhance their learning efficiency.

I began my PhD as a mature student while simultaneously working as a full-time researcher at SZTAKI (the Institute for Computer Science and Control in Hungary), under the supervision of Zoltán Istenes. SZTAKI already had established collaborations with several international institutes, and after my second year, I applied for a French scholarship program, Campus France. I highly recommend such international programs for anyone interested in cross-border academic collaboration, as they offer opportunities to earn double degrees and experience diverse academic environments.

As a result, I was accepted into the École Normale Supérieure des Mines de Paris, a prestigious institution that is part of a collective of leading French universities. I spent nine months there under the supervision of Fabien Moutarde, which was an invaluable experience. It allowed me to gain both technical expertise and cultural knowledge while advancing my research. The opportunity to work with the research teams at SZTAKI, Mines de Paris, and my home institution was invaluable, fostering a collaborative environment that enriched my work.

One key piece of advice I would offer is to approach your PhD as a unique and customizable experience. I strongly recommend seizing every opportunity to present your work at conferences. Presenting at renowned events such as ICRA (International Conference on Robotics and Automation) is an excellent way to network, engage with peers, and receive valuable feedback. I also participated in broader discussions at events like the Future Hungary Conference, which explored the future of robotics and AI. These events are not only valuable for deepening knowledge within your specific field but also for connecting with a wider audience and expanding your professional network.

Alongside my research, I had the privilege of supervising students. Although I did not teach full-time, I mentored five students throughout my PhD, and I continue to work with four of them in my final semester. It was highly rewarding to support their development and see their progress.

Finally, it is important to recognize that a PhD can also be an enjoyable journey. The experience of meeting incredible people, exchanging ideas, and forming lasting professional relationships is one of the most rewarding aspects of this path. Networking opportunities are abundant, and many meaningful collaborations begin during the PhD process, both in research and in more informal settings.

Industrial partner’s insights

Fischl Tamás, Robert Bosch Kft. Research and Development Engineer

PhD research in industrial collaboration at Bosch

My name is Tamás Fischl, and I currently work at the Engineering Center Budapest at Bosch, where I also serve as an industry supervisor at the ELTE Department of Artificial Intelligence. Over the course of my career, I have supported more than 30 thesis topics, collaborating closely with both PhD and master’s students. Within Bosch’s AI-focused department, I hold the role of "Bosch Scientist," acting as a liaison between research efforts and real-world applications.

In this presentation, I will discuss the value of industry-academic collaboration in PhD research, particularly in the context of AI and engineering, and how this partnership can enrich your research experience.

When pursuing a PhD, students typically have an academic supervisor who guides their research. However, in an industrial PhD project, there is often a dual-supervision model: one supervisor from the university and another from the industry partner. This setup introduces a unique challenge, as it requires balancing two different sets of expectations. On the academic side, you must meet the standards for high-quality research publication. On the industrial side, you work towards solving real-world problems that have direct business applications. These objectives do not always align, as what is practical in industry may not necessarily meet the criteria for academic publication, and vice versa. Nonetheless, it is both possible and highly rewarding to bridge these two worlds.

Industry collaboration provides the opportunity to address real-world problems through research, often motivating PhD students with practical issues that can lead to innovative solutions. This integration of practical relevance with academic rigor allows you to produce work that is not only scientifically significant but also immediately applicable in industry.

One of the most rewarding aspects of industrial collaboration is seeing your research transcend theoretical publications and directly impact product development. For instance, developing a solution and later observing its implementation in a product or production line is highly motivating and offers a tangible sense of achievement.

In conclusion, I strongly encourage students to consider industry collaboration as part of their PhD journey. Whether in AI or other fields, working with industry can make your research more dynamic, impactful, and connected to real-world challenges, offering a meaningful path that extends beyond the academic sphere.

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Szabó Róbert, Ericsson Magyarország

PhD-driven Innovation in Tech R&D

I am Róbert Szabó, a Principal Researcher at Ericsson Research, with a background in both industry and academia. Today, I will share how a PhD has played a key role in shaping my career, particularly within the context of industry-academic collaboration and its impact on innovation in technology.

I graduated at the Budapest University of Technology and Economics in 1996, and I completed my PhD there. Following that, I joined Ericsson, where I worked from 2000 to 2006. After that, I returned to academia for seven years to continue my research, before rejoining Ericsson. This back-and-forth between academia and industry highlights the flexibility and broad opportunities that a PhD offers, enabling you to contribute to both sectors and foster innovation.

Currently, I mentor PhD students at ELTE and BME and have firsthand experience of the significant value that industry-academic collaboration brings to the advancement of research and technology. The connection between academic rigor and industrial application is essential for driving innovation, particularly in the rapidly evolving fields of telecommunications and AI.

Ericsson’s Vision and Innovation Ecosystem
At Ericsson, our goal is to create a world with limitless connectivity. With over 100,000 employees across 180 countries, our R&D efforts are spread across 51 global sites, including one of the largest research facilities in Hungary. Proximity to universities plays a vital role in fueling our innovation, as it enables us to access a rich ecosystem of ideas and talent.

Ericsson’s commitment to innovation is reflected in our extensive patent portfolio, with over 60,000 patents, a focus on 5G technology, and ongoing work toward 6G. Our leadership in the global mobile traffic market, with a share approaching 50% outside of China, highlights the global impact of our technological contributions.

R&D in Hungary: A Hub for Talent and Innovation
Since 1991, Ericsson’s Hungarian R&D site has been one of the largest outside Sweden, with over 1,600 employees working in areas such as software development, AI, and telecommunications. This research hub plays a critical role in Ericsson’s global innovation efforts. Notably, approximately 7% of our R&D staff in Hungary hold a PhD, and this group is instrumental in advancing cutting-edge technologies.

At Ericsson Research, we focus on several key areas, including:

• Radio Networks
• AI and Software
• Security
• Digital Representation
• Hardware and Devices

PhDs: A Bridge Between Academia and Industry
A PhD provides a unique advantage, bridging the gap between theoretical knowledge and practical application. While academic research is essential for scientific progress, the ability to apply this research to real-world challenges is where true innovation occurs. In our case, many of Ericsson’s groundbreaking innovations, particularly in mobile communication systems, stem from PhD-driven research. These innovations not only shape the future of communication but also directly impact billions of users worldwide.

Furthermore, PhD holders at Ericsson often play a key role in industry standardization processes, contributing to the development of global technology standards in collaboration with international bodies. This work is both challenging and rewarding, positioning PhD researchers at the forefront of technological advancement.

Innovation at Ericsson Hungary: Leading the Way
Ericsson’s research team in Hungary has made significant contributions to mobile communication technology, earning recognition with multiple innovation awards. The team has been involved in developing and patenting technologies that have become foundational to modern mobile networks.

Over 75% of our research staff in Hungary hold a PhD, demonstrating the critical role that advanced research plays in our continued success and leadership in the telecom industry. The contributions of our PhD researchers are pivotal to maintaining Ericsson’s competitive edge in the market.

Looking Ahead: Co-Creation and Future Opportunities
As we look toward the future, collaboration remains a central focus of our innovation strategy. Ericsson actively collaborates with universities, startups, and other industries to co-create solutions that push the boundaries of technology. This collaborative approach provides PhD students and early-career researchers with the opportunity to make a significant impact on the future of mobile communications, AI, and more.

At Ericsson, we are always seeking talented individuals to join our team and contribute to pioneering research. Whether you are beginning your PhD journey or are interested in applying your expertise in an industrial setting, there are ample opportunities at Ericsson for you to make a lasting impact.

Why Choose Ericsson for Your PhD Journey?

• Cutting-Edge Research: Engage in groundbreaking work on 5G, 6G, AI, and other advanced technologies that will shape the future of connectivity.
• Global Impact: Your research could influence millions of people worldwide, directly contributing to the evolution of mobile communication.
• Career Flexibility: A PhD offers the flexibility to transition between academia and industry, opening doors to a diverse range of career paths.
• Collaboration: Ericsson offers a dynamic and collaborative environment, where you can work alongside universities, startups, and other industries to co-create innovative solutions.

In conclusion, a PhD offers unparalleled opportunities for research-driven innovation, and at Ericsson, we are committed to fostering a collaborative and forward-thinking environment. We look forward to connecting with future PhD researchers and creating a lasting impact on the world of technology.

Think tank

Prof. Turcsányi Szabó Márta

AI in Research: Tool, Teammate, or Threat?

Should You Use AI in Your PhD Journey?
The integration of AI into research is an increasingly discussed topic, particularly regarding its role in PhD work. As AI tools become more prevalent, it’s easy to consider them as a potential solution for various aspects of the research process, such as writing, structuring, and analyzing research papers. However, the question remains: to what extent should PhD researchers rely on AI?

The Role of AI in the PhD Process
In the context of PhD research, the journey begins with identifying a research gap—an unsolved problem that the dissertation will aim to address. This is a deeply intellectual and creative process that cannot be replaced by AI. While AI can be a useful tool for organizing information and assisting with technical aspects such as data analysis or grammar correction, it cannot independently define the research questions or conceptualize the underlying problems your dissertation seeks to solve.

Key PhD Research Stages:

1. Identifying the Research Gap: At the core of any PhD dissertation is the identification of a research gap—an area of knowledge that is underexplored or unresolved. AI cannot discern these gaps on its own; it is up to the researcher to engage in critical thinking to position their work within the existing academic landscape.
2. Structure and Conceptualization: A research paper is often not written in a linear fashion. The introduction and abstract, for example, are usually among the last sections written. First, researchers must delve into the background of the topic, understanding existing problems, and framing their research in a broader context. While AI tools may assist in organizing and structuring thoughts, the conceptualization and intellectual formulation of ideas must come from the researcher.
3. Interactive Learning and Collaboration: The research process is inherently collaborative. You will discuss ideas with peers, mentors, and experts in the field. AI can aid in brainstorming sessions, but it is your critical thinking, reflections, and personal insights that propel the research forward.
4. Curiosity and Originality: A PhD requires an intrinsic drive to explore unknown areas and challenge existing paradigms. While AI can support tasks like data analysis and language refinement, it cannot replace the intellectual curiosity that drives genuine discoveries.

AI as a Tool, Not a Crutch
AI can undoubtedly serve as a valuable tool throughout the PhD journey. It can help with data analysis, identifying relevant literature, and even refining the language of written work. However, it is crucial to understand that AI is not a substitute for the core intellectual work that defines a PhD. The researcher is responsible for the originality and quality of the research output.

AI tools can occasionally pull data from unclear or unreliable sources, which can result in issues such as plagiarism or the inclusion of inaccurate information. Therefore, it is vital to critically assess and verify any content generated by AI.

Visualizing Your Research
A useful exercise for PhD students is to create mind maps or research diagrams to help visualize complex ideas and track progress. This helps organize and clarify thoughts, ensuring that the research direction is well-defined. Additionally, practicing explaining your research in simple terms—such as creating two “elevator pitches,” one for a CEO and another for a general audience—can be a powerful tool for refining your ideas.

The Ethics of AI in Research
When using AI, it is essential to be mindful of ethical considerations. Researchers should avoid inputting sensitive or confidential data into AI tools and should understand the legal and ethical implications of using AI in their work. This includes copyright laws and ensuring that AI-generated content is checked for accuracy. Ultimately, the responsibility for the research lies with the researcher, who must ensure both the originality and integrity of their work.

Conclusion: The Role of AI in Future Research
Looking ahead to 2030, the future of research will increasingly require skills in AI literacy, creativity, analytical thinking, and problem-solving. Although AI will play a larger role in assisting researchers, it is important to remember that the intellectual contributions—critical thinking, originality, and the ability to navigate complex research questions—remain the responsibility of the researcher.

AI may assist in many aspects of research, from brainstorming to data analysis, but it will never replace the need for the researcher’s original thought process and intellectual contributions. As research tools evolve, the role of AI will become more integrated, but the core of a PhD journey will always remain rooted in curiosity, critical thinking, and the pursuit of knowledge.