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Join our PhD program and become an expert in artificial intelligence. Dive into challenging research topics with world-class mentors and industry partners. We welcome curious minds to join our open culture.
Take advantage of an inspiring environment and become a top expert in your selected AI topic. At Kempelen institute, we prioritise ethical responsibility and societal benefit.
When working on your dissertation, you’ll have access to a distinguished supervising team, including industrial partners and/or renowned experts from world-class research institutions.
Choose a research topic that deeply interests you and will enrich the field. At KInIT, we support excellent, original world-class research and the connection with industry and current research and innovation challenges.
Our culture is based on trust, openness and respect. That is how great ideas are born. We share knowledge and innovations not only within the institute, but also with our industry partners and other curious people like you.
You’ll be teamed up with the best researchers in the region and gain invaluable experience and contacts from abroad that will boost your career growth in industry or academia.
You will become a full-time KInIT employee. We will give you all the time and space you need to focus on your research, skills development and growth.
Supervising team: Mária Bieliková (KInIT guarantor), Jana Kosecka (George Mason University), Branislav Kveton (Google Research), Peter Richtárik (KAUST), Martin Takáč (Mohamed Bin Zayed University), Peter Tino (University of Birmingham), Peter Dolog (Aalborg University)
Key words: machine learning, deep learning, learning theory, optimization, trustworthiness
Machine learning is in the centre of research of artificial intelligence. Many researchers worldwide are dealing with the topics related to machine learning, both in academia and industry. This very dynamic field is characterized with fast transfer of solutions into practical use.
The topics in this domain are defined by premier scientific conferences, where top-class researchers meet, for example ICML (International Conference on Machine Learning), NeurIPS (Advances in Neural Information Processing Systems), IJCAI (International Joint Conference on AI), COLT (Conference on Learning Theory).
This thesis will be advised by an external mentor, who will also define its particular topic.
Interesting research challenges are contained within (but are not limited to) these topics:
There are many application domains, where advanced machine learning methods can be deployed.
Supervising team: Michal Kompan (supervisor, KInIT), Mária Bieliková (supervisor, KInIT), Peter Brusilovsky (University of Pittsburgh), Branislav Kveton (Google Research), Peter Dolog (Aalborg University), Luigi’s Box
Keywords: personalised recommendation, biases, machine learning, user model, fairness, off-policy
The recommender systems are an integral part of almost every modern Web application. Personalized, or at least adaptive, services have become a standard that is expected by the users in almost every domain (e.g., news, chatbots, social media, or search).
Obviously, personalization has a great impact on the everyday life of hundreds of million users across many domains and applications. This results in a major challenge – to propose methods that are not only accurate but also trustworthy and fair. Such a goal offers plenty of research opportunities in many directions:
There are several application domains where these research problems can be addressed, e.g., search, e-commerce, news, and many others.
Supervising team: Mária Bieliková (supervisor, KInIT), Ivan Srba (KInIT)
Keywords: machine learning, meta-learning, transfer-learning, weakly supervised learning, semi-supervised learning, zero/one-shot learning
Ubiquity of computing is nowadays generating vast datasets potentially usable for training machine learned models. Yet, these data mostly come not annotated, i.e. without labels necessary for their use for supervised (most conventional) training methods (e.g. classification). Conventional acquisition of proper labels using human force is a costly endeavour. The result is that proper labels are either unavailable, few or of poor quality.
To circumvent the issue, many approaches are emerging and are presently researched by many researchers: meta-learning, transfer-learning, weakly supervised learning, zero/one-shot learning, semi-supervised learning. Each of these fields (or combination thereof) presents opportunities for new discoveries. Orthogonal to this, explainability and interpretability of models is an important factor to consider and advances in this regard are welcome (generally in AI and particularly in the mentioned approaches).
There are several domains of applications, where research of methods and models for addressing small labelled data can be applied. These include (but are not limited to) false information detection, auditing of social media algorithms and their tendencies for disinformation spreading, and support of manual/automated fact-checking.
Supervising team: Jakub Šimko (supervisor, KInIT), Peter Brusilovsky (University of Pittsburgh), Jana Kosecka (George Mason University), Peter Dolog (Aalborg University)
Keywords: machine learning, human in the loop, crowdsourcing, human computation, games with a purpose, active learning.
The models created in machine learning can only be as good as the data on which they are trained. Researchers and practitioners thus strive to provide their training processes with the best data possible. It is not uncommon to spend much human effort in achieving upfront good general data quality (e.g. through annotation). Yet sometimes, upfront dataset preparation cannot be done properly, sufficiently or at all.
In such cases the solutions, colloquially denoted as human-in-the-loop solutions, employ the human effort in improving the machine learned models through actions taken during the training process and/or during the deployment of the modes (e.g. user feedback on automated translations). They are particularly useful for surgical improvements of training data through identification and resolving of border cases. This is also directly related to explainability and interpretability of models.
Human-in-the-loop approaches draw from a wide palette of techniques, including active and interactive learning, human computation, crowdsourcing (also with motivation schemes of gamification and serious games), and collective intelligence. Each of these fields (or combination thereof) presents opportunities for new discoveries. They border on computer science disciplines such as data visualization, user experience (usability in particular) and software engineering.
The domains of application of machine learning with human-in-the-loop are predominantly those with a lot of heterogeneity and volatility of data. Such domains include online false information detection, online information spreading (including spreading of narratives or memes), auditing of social media algorithms and their tendencies for disinformation spreading, support of manual/automated fact-checking and more.
Supervising team: Viera Rozinajová (supervisor, KInIT), Anna Bou Ezzedine (KInIT), Gabriela Grmanová (KInIT)
Key words: Data analytics, machine learning, optimization, anomaly detection, smart grid
Thanks to the deployment of new technologies in our daily lives, a huge amount of data of various types is constantly being generated. The created datasets need to be processed efficiently – the information contained in them often supports the correctness and accuracy of decision-making processes. The development of data analysis methods is therefore an important part of IT research. For various reasons, traditional processing methods are not generally applicable, so new approaches need to be sought. They are mostly based on artificial intelligence and machine learning.
Intelligent data analysis has great potential for solving current research tasks in many areas, including the energy domain. This field is undergoing great changes – renewable energy sources, batteries and electromobility convert the nature of the electricity grid and the classic one-way centralized network is becoming a two-way distributed network. This fact raises a number of research questions and problems to solve.
Research topics include:
However, data analysis in a broader sense can be of interest – it is possible to focus on the tasks of prediction, clustering, classification or detection of anomalies in different domains.
Supervising team: Marián Šimko (supervisor, KInIT), Jana Kosecka (George Mason University)
Keyword: deep learning, multilingual learning, transparency, interpretability, information extraction, text classification, language models
Continuous increase of data available world-wide emphasizes the need of its automatic processing and understanding. Particular challenges are posed by the heterogeneous and unstructured nature of text content provided in natural language. Natural language processing (NLP) ranks among the most prospective subfields of artificial intelligence with great potential for innovative applications affecting everyday life.
Recent advances in neural networks and machine learning allowed to push efficiency and scope of natural language understanding and generation forward. Yet, there remain many research challenges related to particular subtasks, application domains and languages. Further research and various resulting phenomena exploration is necessary. Special attention is drawn by the issues of interpretability and transparency of NLP models or by novel paradigms of learning addressing the problem of low-resource languages.
Particularly interesting challenges include, but are not limited to:
We provide PhD study in collaboration with the Faculty of Information Technology at VUT, Brno (Czechia). You’ll be enrolled at the faculty as an external doctoral student. After you’re accepted for study, you’ll work as a full-time employee at our institute. Your doctoral research will be your primary job role.
You can choose from multiple interesting topics of your dissertation. They are based on different mentoring schemes:
You’ll have access to an expert supervising team. Depending on the selected scheme, it can include industrial partners and/or a scientist from a renowned foreign university.
While working at our institute you’ll be a member of a KInIT research team. The teams collaborate on various projects or on other activities:
The rest is up to you! Turn your curiosity and passion for intelligent technologies into new discoveries or inventions making the world a better place.
Mária Bieliková is an expert researcher at KInIT. She focuses on human-computer interaction analysis, user modeling and personalization. Recently, she has been working in data analysis and modeling of antisocial behavior on the Web. She is active in discussions on trustworthy AI at the national and European levels. Maria has supervised 19 successful doctoral graduates to date. She co-authored 70+ journal publications, 200+ conference papers, received 4,400+ citations (Google Scholar h-index 30), and serves on the editorial board of two CC journals. She has been the principal investigator in 40+ research projects.
Michal Kompan is an expert researcher at KInIT. He focuses on recommender systems, machine learning, user modeling, and information retrieval. His research is focused on predictive modeling and customer behavior (e.g., churn prediction, next-item recommendation), as well as content-based adaptive models. Michal serves as a reviewer or/and program committee member at several international conferences, such as RecSys, SIGIR, WWW, ADBIS, Hypertext, UMAP and SMAP.
Viera Rozinajová is an expert researcher at KInIT. She is focusing on intelligent data analysis, particularly predictive modeling, cluster analysis, anomaly detection and optimization. Before her employment at KInIT, she worked as an associate professor at the Faculty of Informatics and Information Technologies at the Slovak University of Technology in Bratislava, where she headed up the Big Data Analysis group. She has authored/co-authored more than 70 publications in scientific journals and conferences and has participated in more than 25 national and international research projects and has led several of them.
Jakub Šimko is an expert researcher at KInIT, where he also leads the Web and User Data Processing team. Jakub focuses on the intersection of human computation, machine learning and user modeling. He has recently been working on social media algorithm auditing, misinformation modeling and promotes interdisciplinary approaches to computer science research. He graduated from Slovak University of Technology in Bratislava, where, after receiving his PhD, he worked for 7 years as a researcher and teacher. He co-authored more than 30 internationally recognized publications, together receiving more than 350 citations.
Marián Šimko is an expert researcher at KInIT. Marián focuses on natural language processing, information extraction, low-resource language processing and trustworthiness of neural models. He is a former vice-dean for Master’s study and alumni co-operation at the Slovak University of Technology.
Peter Brusilovsky is a Professor at the School of Computing and Information, University of Pittsburgh, where he directs the Personalized Adaptive Web Systems (PAWS) lab. His research is focused on user-centered intelligent systems in the areas of adaptive learning, recommender systems, and personalized health. He is a recipient of Alexander von Humboldt Fellowship, NSF CAREER Award, and Fulbright-Nokia Distinguished Chair. Peter served as the Editor-in-Chief of IEEE Trans. on Learning Technologies, and a program chair for several conferences including RecSys.
Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. His current research interests include machine learning and data mining in the areas of user behavior analysis and prediction, recommender systems, preference learning, and personalization. Peter is a senior member of ACM, served as a senior program commitee member of AI related conferences as well as a general chair of UMAP, HT and Web Engineering conferences.
Jana Kosecka is a Professor at the George Mason University. She is interested in computational models of vision systems, acquisition of static and dynamic models of environments by means of visual sensing, high-level semantic scene understanding and human-computer interaction. She held visiting positions at UC Berkeley, Stanford University, Google and Nokia Research, and served as Program chair, Area chair or senior member of editorial board for leading conferences in the field CVPR, ICCV, ICRA.
Jana is currently mentor of our PhD student: Ivana Beňová
Branislav Kveton is a Principal Scientist at Amazon’s lab in Berkeley. He proposes, analyzes, and applies algorithms that learn incrementally, run in real time, and converge to near-optimal solutions as they learn. He made several fundamental contributions to the field of multi-armed bandits. His earlier work focused on structured bandit problems with graphs, submodularity, and low-rank matrices, and ranked lists. His recent work focuses on making bandit algorithms practical
Peter Richtárik is a Professor of Computer Science & Mathematics at KAUST. He is one of the founders and a Fellow of the Alan Turing Institute. Through his work on randomized and distributed optimization algorithms, he has contributed to the foundations of machine learning and federated learning. He serves as an Area Chair of leading machine learning conferences, including NeurIPS, ICML and ICLR.
Martin Takáč is an Associate Professor and Deputy Department Chair of Machine Learning Department at MBZUAI, where he is a core faculty at the Optimization & Machine Learning Lab. His current research interests include the design and analysis of algorithms for machine learning, applications of ML, optimization, HPC. He serves as an Area Chair of ML conferences such as AISTATS, ICML, and NeurIPS.
Peter Tino is a Professor at the School of Computer Science, University of Birmingham, UK. He is interested in the interplay between mathematical modelling and machine learning (dynamical systems, probabilistic modelling, statistical pattern recognition, natural computation). Peter is interested in both foundational aspects and applications in interdisciplinary contexts (e.g. astrophysics, biomedical sciences, cognitive neuroscience).
He is a Fellow of the Alan Turing Institute and has served on editorial boards of leading journals such as IEEE TNNLS, IEEE TCYB, Neural Networks and Scientific Reports.
Transform your curiosity into excellence. If you are interested in doing a PhD degree at KInIT, let us know as soon as possible. Fill in the PhD @ KInIT expression of interest form not later than March 31st.