Postdoctoral Researcher and Research Fellow Positions in ICT (Helsinki, Finland)

Aalto University and the University of Helsinki are looking for Postdoctoral Researchers and Research Fellows in multiple areas of ICT, including:

  • artificial intelligence and machine learning
  • data science
  • privacy and security
  • computational health
  • human-computer interaction
  • natural language processing

We welcome applications linking two or more of these areas together. We are committed to fostering an inclusive environment with people from diverse backgrounds, and researchers from underrepresented groups are particularly encouraged to apply.

The deadline for applications is January 27th, 2020 (midnight UTC+02:00). By applying to this call, organized by Helsinki Institute for Information Technology HIIT and Finnish Center for Artificial Intelligence FCAI, you apply with one application to both Aalto University and the University of Helsinki. Aalto University and the University of Helsinki are the two leading universities in Finland in computer science and information technology. Both are located in the Helsinki Metropolitan area, and the employing university will be determined according to the supervising professor. Please see below for additional information on the application process.

Helsinki Institute for Information Technology HIIT and Finnish Center for Artificial Intelligence FCAI bring together the world-class expertise of Aalto University and the University of Helsinki in joint research centers and programs. There are multiple research groups participating in each of them. The researchers to be recruited will work in one of the participating groups, or in some cases a shared position may also be possible. In addition, positions are available in the specific projects listed below.

Research centers and programs

The research of Finnish Center for Artificial Intelligence FCAI is spearheaded by seven Research Programs and five Highlights with multiple research groups involved in each. Highlights exist to make sure the fundamental research in Research Programs is taken into use.

FCAI Research Programs:

  1. Agile probabilistic AI develops an interactive and AI-assisted process for building new AI models with practical probabilistic programming. Read more at

    Keywords: Probabilistic programming; robust and automated Bayesian machine learning.

    Several professors contribute. Coordinating professor: Aki Vehtari

  2. Simulator-based inference develops methodology for the new AI having efficient, interpretable reasoning capability, by cross-breeding modern machine learning and simulator-based inference. Read more at

    Keywords: Approximate Bayesian computation ABC; likelihood-free inference; generative adversarial networks (GAN); applications in many fields including medicine, materials design, visualization, and business.

    Several professors contribute. Coordinating professor: Jukka Corander

  3. Next generation data-efficient deep learning develops methods which harness the power of deep learning while achieving good results with less training data and in particular less human supervision. Read more at

    Keywords: Deep reinforcement learning; semi-supervised learning; simulation methods; Bayesian deep learning.

    Several professors contribute. Coordinating professor: Arno Solin

  4. Privacy-preserving and secure AI develops the new principles and techniques needed for privacy-preserving machine learning and the tools for building trustworthy and secure AI systems. Read more at

    Keywords: Privacy-preserving machine learning; differential privacy; adversarial machine learning.

    Several professors contribute. Coordinating professor: Antti Honkela

  5. Interactive AI enables AI that people can naturally work and solve problems with, and which demonstrates the ability to better understand our goals and abilities, takes initiative more sensitively, aligns its objectives with us, and supports us. Read more at

    Keywords: Interactive machine learning; reinforcement learning and computational rationality; cognitive modelling; probabilistic programming for behavioral sciences.

    Several professors contribute. Coordinating professor: Antti Oulasvirta

  6. Autonomous AI addresses the fundamental challenges of long-term autonomous operation, in particular, how learning and planning can be performed to ensure safe operation over long time horizons. Read more at

    Keywords: Autonomous systems; Reinforcement learning; Model predictive control.

    Several professors contribute. Coordinating professor: Ville Kyrki

  7. AI in Society focuses on social and ethical dimensions of AI. It deals both with the preconditions of trustworthy and socially acceptable AI and the consequences of uses of AI. It aims to bring together AI research and human sciences to better understand how AI works in organizations and society. Read more at

    Keywords: Design and domestication of AI; Understandability of AI; Foresight and responsibility in AI decision-making and robotics; Legitimacy and social acceptability of AI.

    Several professors contribute. Coordinating professor: Petri Ylikoski

FCAI Highlights:

  1. Easy and privacy-preserving modeling tools has the main objective to measure and maximize the impact of FCAI research on the process of probabilistic AI development. Read more at

    Several professors contribute. Coordinating professor: Arto Klami

  2. Applications of AI in healthcare creates AI tools to tackle real-world problems in healthcare together with expert collaborators from the respective fields. Read more at

    Application 1: AI for genetics
    Application 2: Computational design of vaccines
    Application 3: Deep learning for healthcare resource allocation

    Several professors contribute. Coordinating professor: Pekka Marttinen

  3. Intelligent service assistant for people in Finland has a mission to deploy real AI services for wide audience in Finland. The Highlight is directly linked to AuroraAI initiative ( Read more at

    Several professors contribute. Coordinating professor: Tommi Mikkonen

  4. Intelligent urban environment focuses on how to combine (i) measurements from natural environment, (ii) simulations, and (iii) modeling in order to, e.g., make decisions in interaction with the user (e.g., “what-if-engines”) and to model and understand observed and/or simulated processes. This includes applications of Interactive AI, Agile probabilistic AI and simulators to model measurements and simulator outputs from urban environments. Read more at

    Several professors contribute. Coordinating professor: Kai Puolamäki

  5. AI-driven design of materials develops AI technology for accelerated materials design and characterization. Read more at

    Several professors contribute. Coordinating professor: Patrick Rinke

Helsinki Institute for Information Technology HIIT supports research centers and programs in data science, information security and computational health along with artificial intelligence research that is not in the current scope of FCAI.

HIIT Focus Areas:

  1. Other AI. We also welcome applications from areas of artificial intelligence research that are not covered by the FCAI Programs and Highlights listed above.

    Director: Professor Petri Myllymäki

  2. Helsinki Centre for Data Science (HiDATA). Data science, i.e., extraction of knowledge and insights from data, is important across many fields of science. HiDATA, Helsinki Centre for Data Science, aims to create a world-class research and research-based education hub of data science in Helsinki, as a joint effort between the University of Helsinki and Aalto University. HiDATA builds on the existing, strong research in various areas of data science, and aims to provide novel synergies across disciplines. HiDATA is funded in 2017-2021 as part of the profiling measures of the Academy of Finland, the University of Helsinki and Aalto University.

    Director: Professor Sasu Tarkoma

  3. HAIC Research Program (HAIC-R). HAIC (Helsinki-Aalto Center for Information Security) is a strategic initiative set up by Aalto University and the University of Helsinki in June 2016 to ensure excellence in information security research and education. During the last few years, Aalto University and the University of Helsinki have built up strong research groups and education programs in information security and privacy. HIIT hosts the research arm of HAIC as a research program. The long-term mission of the HAIC Research Unit towards the year 2025 is to enable the design, building and deployment of distributed large-scale systems, where each node (component, device, network element etc.) contributes in verifying trustworthiness of the entire system. Each node would also be able to verify whether any other node or even the entire system is trustworthy.

    Director: Professor Valtteri Niemi

  4. Foundations of Computational Health (FCHealth). The FCHealth programme aims to solve hard computational challenges faced upon the emerging digitalization and wide adoption of data-driven approaches in healthcare. We combine state-of-the-art computational methods with large-real world data arising in healthcare and personalized medicine, analysed in collaboration with experts from Aalto University, University of Helsinki, Hospital District of Helsinki and Uusimaa (HUS) as well as Institute for Molecular Medicine Finland (FIMM).

    Director: Professor Juho Rousu

Specific projects

17. Natural language generation, Professor Hannu Toivonen and Dr. Mark Granroth-Wilding, Department of Computer Science, University of Helsinki

We are looking for a postdoc to join the Discovery Research Group and its two European projects, NewsEye and Embeddia. Both projects revolve around analysis of news stories and generation of new text. NewsEye has a focus on old newspaper archives while Embeddia works with current news. We are now looking for candidates primarily for natural language generation (NLG). NewsEye will involve collaboration with Digital Humanities researchers. The background of an ideal candidate contains both computer science and natural language processing/language technology; previous experience with NLG is not required. Competence in a variety of machine learning techniques is desirable. We offer opportunities for both international and cross-disciplinary collaboration.

18. Data Science for Natural Sciences, Professor Kai Puolamäki, Department of Computer Science, University of Helsinki

The Exploratory Data Analysis group is looking for a postdoctoral researcher. The topics of interest include the development of AI methodology to analyze and model the underlying processes in data sets produced by physical measurements and simulators, modelling of the user’s knowledge, interpretable models, and human-computer interfaces in data analysis; the exact topic can be tailored taking the expertise and interests of the applicant into account. Part of the work may be done in collaboration with Institute for Atmospheric and Earth System Research (INAR). The position requires a relevant doctoral degree, e.g., in computer science, statistics, mathematics, or physics, and ability to work in an environment with English as a working language.

Please contact Prof. Kai Puolamäki ( for further information.

19. Next-generation databases powered with machine learning techniques, Professor Jiaheng Lu, Department of Computer Science, University of Helsinki

We are looking for a postdoc researcher to develop methods for next-generation database systems powered by machine learning methods. Applicants are expected to have a background in database and/or machine learning research.

For more information contact Jiaheng Lu (

Group website:

20. Computational methods in personalized medicine, Professor Sampsa Hautaniemi, University of Helsinki

The Hautaniemi group focuses on understanding and finding effective means to overcome drug resistance in cancers. Our approach is to use systems biology, i.e., analyze molecular & clinical data from cancer patients with machine learning and mathematical methods, to identify efficient patient-specific therapeutic targets. We coordinate several large personalized medicine consortia, which enables rigorous validation of the predictions from computational analyses as well as translating results to clinic. We have projects available in the fields of image analysis, causal analysis, multi-variate statistical analysis and data integration.

More information and recent publications: Hautaniemi lab.

Please contact Prof. Sampsa Hautaniemi for further information.

21. Machine learning and differential privacy, Professor Antti Honkela, Department of Computer Science, University of Helsinki

Differential privacy allows developing machine learning algorithms with strong privacy guarantees. In this project, you will join our group in developing new learning methods operating under these guarantees, and applying them to real-world problems. Collaboration opportunities will enable testing the methods on academic and industrial applications.

More information and papers: or by email (

22. Federated data sovereignty, semantic interoperability, and data consistency with interledger technologies, Professor Pekka Nikander, Department of Communications and Networking, Aalto University

We are looking for a post doc interested in working on state-of-the-art DLTs and interledger technologies. With the introduction of distributed ledger technologies (DLTs and blockchains), it has become possible to create new fully decentralized governance models. When implementing such new governance models, especially for systems that involve IoT, the questions of data sovereignty, semantic interoperability, and federation become crucial. In this research, these will be considered in a dynamic and distributed setting involving multiple interconnected blockchains, i.e., in an interledger technology setting. The research will also involve implementation of the needed software components. A successful applicant is expected to have a strong (proven) background in professional-level software development, basic skills in software security and preferably some knowledge on DLTs and smart contracts. Your role and main goal in this work is to design, study, and implement interledger systems in the on-going PHOENIX project context ( Your tasks will also include acting as a task leader in the project.

For more information, please contact Dr. Pasi Lassila (

23. Theory of distributed and parallel computing, Professor Jukka Suomela and Professor Jara Uitto, Department of Computer Science, Aalto University

The research groups led by Jukka Suomela and Jara Uitto in the Department of Computer Science at Aalto University are looking for postdoctoral researchers to work on the theoretical foundations of distributed and parallel computing.

The goal is to broaden our understanding of models of computing where the communication network is well-connected but the bandwidth is limited, such as the k-machine model, congested clique, BSP, and MPC. Our current research focuses on two main objectives:

  • Computational complexity of statistical inference problems. In particular, we are interested in designing algorithms and proving lower bounds for problems related to sparse matrix multiplication and sparse linear systems.
  • Computational complexity of clustering problems and graph problems. In particular, we aim to design new algorithms for basic primitives such as finding large independent sets and matchings.

The candidates are expected to have a doctoral degree and an excellent track record in research related to theoretical computer science, demonstrated by relevant publications in leading conferences or journals.

24. Machine Learning for Health (ML4H), Professor Pekka Marttinen, Department of Computer Science, Aalto University

Recent years have witnessed an accumulation of massive amounts of health related data, enabling researchers to address diverse problems such as: how to allocate healthcare resources fairly and efficiently, how to provide personalized guidance and treatment to users based on real-time data from wearable self-monitoring devices, or how to use genomic data to understand disease or antibiotic resistance. Central challenges in ML4H include analysing massive amounts of diverse data from multiple data sources, going beyond correlation to learn about causal relations between relevant variables, interpreting the models, and assessing the uncertainty of predictions, to name a few. We tackle these by developing new models and algorithms which leverage on modern principles of machine learning, using techniques such as deep neural networks, probabilistic methods, interactive machine learning, attention, and generative models. Examples of our ongoing interdisciplinary projects include: analysis of nationwide healthcare register data, mobile health, genomics, antibiotic resistance, and epidemiology. Successful applicants are expected to have an outstanding record in machine learning, statistics, applied mathematics, or a related field, and a passion to put these skills to use in interdisciplinary research to address some of the most burning challenges in today’s society.

25. Model-Management Systems, Professor Michael Mathioudakis, University of Helsinki

The widespread use of machine learning has created a demand for systems that allow analysts to focus on specifying predictive tasks while having limited direct involvement in lower-level decisions for the management of data, models, and computational resources.

This project develops a computational framework and algorithms towards such systems.

Requirements: strong analytical and coding skills; publication record in machine learning, data management, or related field.

26. Human Sciences – Computing Interaction, Professor Eetu Mäkelä, Helsinki Centre for Digital Humanities, University of Helsinki

At the Helsinki Centre for Digital Humanities (HELDIG), we desire people interested in challenging their computational expertise against the complex questions and datasets of society and humanity. Application areas include how the relationship between politics and media has developed in the 21st century, how claims to fame are argued in EU food origin protection requests, what the geographical and dialectical distribution of themes in folk poetry is, how the public sphere developed in the 18th and 19th centuries, and more.

More information:

27. Security engineering, usable security and human-computer interaction, Professor Janne Lindqvist, Department of Computer Science, Aalto University

We are looking for post-docs interested in security engineering, usable security and human-computer interaction. Background and interest in systems security, security engineering, data science, machine learning, modeling, human-computer interaction or social and behavioral sciences is required. PhD in a relevant field is required.

Examples of work done in the group can be found at

28. Understanding video streaming user experiences, Professor Janne Lindqvist, Department of Computer Science, Aalto University

We are looking for post-docs interested in understanding video streaming user experiences. Background and interest in measuring user experience, modeling, human-computer interaction, computer science or social and behavioral sciences is required. PhD in a relevant field is required.

Examples of work done in the group can be found at

29. New HPC tools to model solar magnetism, Professor Maarit Käpylä, Department of Computer Science, Aalto University

The recently awarded ERC Consolidator Grant project “UniSDyn” has an aim to construct novel computational tools to model the dynamo mechanism in solar-like stars. This is extremely important, as the solar dynamo is responsible for driving space weather and climate, and hence strongly influences our hi-tech society, but constitutes a notoriously difficult problem theoretically and numerically. Capturing the whole Sun in a computer is virtually impossible even with the modern supercomputers, if standard massively parallel computing paradigm with CPUs, using brute force direct numerical simulations, is considered. Therefore, radically new ideas are required! Creating new, more efficient and sustainable, computational tools with accelerator platforms, and using intelligent data analysis methods to characterize and learn the effects that the small-scale dynamics has on the large-scales, to be able to build sub-grid scale models that enable us to create simulation tools in the large-eddy simulation framework, are plausible directions that we want to take in this project. Join our multidisciplinary group consisting of computer scientists, mathematicians, and astrophysicists, and bring in more ideas and expertise! We are currently recruiting both a PhD student and a postdoctoral researcher.

To read more about us, please visit

30. Edge and Fog Computing, Professor Mario Di Francesco, Department of Computer Science, Aalto University

We are looking for a postdoctoral researcher within a research project on Edge / Fog Computing for the Internet of Things (IoT) funded by the Academy of Finland, the leading Finnish funding agency. The project aims to devise novel mathematical and software tools to optimize the performance of large-scale IoT applications over heterogeneous devices, with a focus on reliable and low-latency operations. The project is a collaboration between Aalto University, the University of Oulu, VTT Technical Research Center of Finland, and several research groups worldwide. Thus, it provides an excellent chance for internationalization, thereby enabling wide opportunities for career development in both industry and academia.

Additional information is available at

31. Bayesian deep learning with applications to biology/medicine, Professor Harri Lähdesmäki, Department of Computer Science, Aalto University

Bayesian inference methods for deep learning models promises to provide robust learning that are not sensitive to overfitting and provide reliable uncertainty estimates. Our recent work include recently proposed deep/nonparametric differential equation models that make it possible to learn arbitrary continuous-time dynamics from data without any prior knowledge. These models can also be used to implement state-of-the-art deep learning methods in the context of deep Gaussian processes or neural networks. These high-capacity continuous-time models are, however, sensitive to over-fitting. We are searching for a postdoc to work on these topics and to further extend the methodologies as well as probabilistic inference methods. Work can focus on pure method development or also include novel applications to biology/medicine (based on applicant’s preference).

For more information and relevant recent work, see or contact any of the academic contact persons listed above.

32. Tools and techniques for quality assurance of AI systems, Professor Jukka K Nurminen, Department of Computer Science, University of Helsinki

We are looking for a postdoc to to work on tools and techniques for the testing of machine learning systems. To ensure that machine learning systems work for real, new ways are needed to ensure their correct and efficient operation as well as their smooth development and maintenance. The candidate is expected to develop, analyze, measure, and model alternative ways to test different kind of machine learning systems and create new ideas and insights based on those. This includes implementing research prototypes to try out ideas and to collect and analyze data. Applicants are expected to have a strong background in either machine learning or software engineering and willingness to learn the other.

Please contact Jukka K Nurminen at for further information.

33. Probabilistic machine learning, Professor Samuel Kaski, Finnish Center for Artificial Intelligence FCAI, Aalto University

I am looking for a postdoc or research fellow to join the Aalto Probabilistic Machine Learning Group, to work on new probabilistic models and inference techniques. I am open to excellent and exciting suggestions, in particular around the following topics:

  • simulator-based inference, for combining first-principles models with learning from data
  • Bayesian deep learning
  • Bayesian reinforcement learning and inverse reinforcement learning.

Can be theoretical or applied work or both; the group has excellent opportunities for collaboration with top-notch partners in multiple applications.

For more information, see

34. Probabilistic interactive user models for interactive AI, Professor Samuel Kaski, Finnish Center for Artificial Intelligence FCAI, Aalto University

Most machine learning systems operate with us humans, to augment our skills and assist us in our tasks. In environments containing human users, or, more generally, intelligent agents with specific goals and plans, the system can only help them reach those goals if it understands them. Since the goals can be tacit and changing, they need to be inferred from observations and interaction. We develop the probabilistic interactive user models and inference techniques needed to understand other agents and how to assist them more efficiently. If I was a postdoc candidate looking for a new promising direction, I would probably choose this one!

Additional keywords: active learning, experimental design, knowledge elicitation, multi-agent learning, machine teaching, reinforcement learning

For more information, see and

35. Probabilistic modelling for personalized medicine and drug development, Professor Samuel Kaski, Finnish Center for Artificial Intelligence FCAI, Aalto University

I am looking for a postdoc or research fellow to join us in developing new probabilistic modelling and machine learning methods needed in the core problems of modern healthcare: developing better drugs and personalizing treatments. For both problems, we combine the ability of modern flexible models to take into account nonlinearities and interactions, with the Bayesian approach to handle the uncertainty in the data and results. Precision medicine needs causal inference and predictive modelling based on genomic and clinical data, and drug development additionally generative models of chemistry; both need adaptive experimental design. This is an excellent opportunity to work with top-notch experts in both medicine (cancer and clinical) and machine learning.

For more information, see

36. Machine Insight for Behavioral Analytics, Professor Arto Klami, Department of Computer Science, University of Helsinki and Professor Antti Oulasvirta, Department of Communications and Networking, Aalto University

We seek two postdoctoral scholars for a project at the intersection of machine learning and behavioral analytics. We aim at the development of inference and visualization methods that can offer *human-like behavioral explanations to business data*. We pursue open source methods that can be widely deployed in analytics. The project team combines our existing experience at FCAI in probabilistic machine learning and statistical modeling with established modeling in behavioral and decision science. The behavioral models are needed as they are able to explain human behavior based on observed data, whereas machine learning is needed to provide powerful methods for solving complex inference tasks required for grounding the behavioral models on observed data. The research is lead by professors Arto Klami and Antti Oulasvirta, but the project is done in close collaboration with other FCAI key professors, including Samuel Kaski, Jukka Corander and Aki Vehtari, to further strengthen the expertise in probabilistic machine learning and simulator-based inference. The project is carried out as part of Finnish Center for Artificial Intelligence (FCAI), which forms the national flagship for AI research. Contracts are offered for 2 years.

37. Robust Machine Learning for Scarce Data and Non-Standard Settings, Professor Teemu Roos, Department of Computer Science, University of Helsinki

Machine learning (ML) has recently achieved a lot in areas where the standard assumptions about the data hold and the amount of training data available is large. However, it still faces many challenges in areas where we would need it the most. We are working on both probabilistic and deep learning methods that work with minimal data (including zero-shot scenarios) and give robust uncertainty estimates even when assumptions (such as i.i.d. data) are violated.

We welcome applicants who have thorough knowledge of probabilistic and/or deep learning approaches with a keen interest in research that ranges from fundamental theory all the way to cutting edge practical machine learning solutions in pharmacology, neuroscience, sports medicine, physics, and other sciences. Familiarity with non-standard, scarce data scenarios is considered a plus but is not required.

All researchers at the University of Helsinki are also expected to participate in some teaching tasks, which can also involve online learning (Elements of AI).

The position is funded by an Academy of Finland grant for two years (2020-2021) with the option to extend for a third year. The project also includes the option to collaborate with other research groups at the Finnish Center for AI (FCAI).

How to apply

The applications are to be submitted through the eRecruitment system. Choose in the application form one or more of the research centers, programs and/or projects described above and explain in the motivation letter how you could contribute in the selected research area(s).

Required attachments:

  • Motivation letter including a tentative plan for the research work (1-5 pages)
  • CV
  • List of publications (please do not attach full copies of publications)
  • A transcript of the doctoral studies and the degree certificate of the PhD degree

All material should be submitted in English. The application materials will not be returned. Short-listed candidates may be invited for an interview either at the Otaniemi or Kumpula campus or for an interview conducted via Skype.


The candidate should have a PhD and is expected to have an excellent track record in scientific research in one or several fields relevant to the position. Good command of English is a necessary prerequisite. In the review process, particular emphasis is put on the quality of the candidate’s previous research and international experience, together with the substance, innovativeness, and feasibility of the research plan, and its relevance to the research group or groups in question. Efficient and successful completion of studies is considered an additional merit.

Compensation, working hours and place of work

The salary for a postdoctoral researcher starts typically from about 3 500 EUR per month depending on experience and qualifications. In addition to the salary, the contract includes occupational health benefits, and Finland has a comprehensive social security system. The annual total workload of teaching staff at the recruiting universities is 1 624 hours.

The position is located at Aalto University’s Otaniemi campus or the University of Helsinki’s Kumpula campus. The positions belong to the university career system and the selected persons will be appointed for fixed-term positions, for postdoctoral researchers typically for two years with an option for renewal. For exceptional candidates, a longer term Research Fellow position can be considered. The length of the contract and starting and ending dates are negotiable. In addition to research work, the persons hired are expected to participate in the supervision of students and teaching following the standard practices of the hiring department.

About Helsinki

The Helsinki Metropolitan area forms a world-class information technology hub, attracting leading scientists and researchers in various fields of ICT and related disciplines. Moreover, as the birth place of Linux, and the home base of Nokia/Alcatel-Lucent/Bell Labs, F-Secure, Rovio, Supercell, Slush and numerous other technologies and innovations, Helsinki is becoming one of the leading technology startup hubs in Europe. Finland tops international rankings in education, equality and safety, and Helsinki, the capital of Finland, is regularly ranked as one of the most livable cities in the world.

About the host institutions

Aalto University is a community of bold thinkers where science and art meet technology and business. We are committed to high-quality research with significant impact on the international scientific community, industry and business, as well as the society at large. Aalto is an international community: more than 30% of our academic personnel are non-Finns. Aalto University has six schools with nearly 20 000 students; it is in world’s top-10 of young universities (QS Top 50 under 50).
For more information, see

The University of Helsinki, established in 1640, is one of the world’s leading universities for multidisciplinary research. The University of Helsinki is an international academic community of 40,000 students and staff members. The university lays special emphasis on the quality of education and research, and it is a member of the League of the European Research Universities (LERU). For more information, see

Helsinki Institute for Information Technology HIIT is a joint research institute of Aalto University and the University of Helsinki for basic and applied research on information technology. HIIT’s mission is to conduct top-level research, seamlessly moving between fundamental methods and technologies to novel applications and their impact on people and society. For more information, see

Finnish Center for Artificial Intelligence FCAI brings together the world-class expertise of Aalto University and the University of Helsinki in AI research, strengthened further with an extensive set of companies and public sector partners. FCAI has been selected as one of the prestigious Flagships of the Academy of Finland, a status granted to very few selected centers of excellence with high societal impact. The total budget of FCAI is 250 M€ over the next 8 years. For more information see

Further information

  • Research related questions: The leader of the research center/program/project specified above
  • Application process and practicalities: Tiina Torvinen (