Postdoc and Doctoral student positions in Machine Learning

Finnish Center for Artificial Intelligence (FCAI) is searching for exceptional doctoral students and postdoctoral researchers to tackle complex and exciting problems in the field of machine learning. Come and join us to create the next generation of AI that is data-efficient, trustworthy and understandable!

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, creating an attractive, world-class ICT hub in Helsinki metropolitan area. Hundreds of researchers are involved in various research and educational activities, and tens of industrial actors are collaborating in joint initiatives. Moreover, as the birth place of Linux, and the home base of Nokia/Alcatel-Lucent/Bell Labs, F-Secure, Rovio, Supercell, Slush (the biggest annual startup event in Europe) and numerous other technologies and innovations, Helsinki is fast becoming one of the leading technology startup hubs in Europe.

FCAI research agenda builds on our world-class expertise in machine learning, and is spearheaded by 5 research programs with multiple research groups involved in each.

FCAI is currently hiring doctoral students and postdoctoral researchers in the following FCAI research programs and the detailed projects listed below.


Research programs

(for more information see

1. Agile probabilistic AI. Keywords: Probabilistic programming; Robust and automated Bayesian machine learning.

Coordinator: Aki Vehtari

2. Simulator-based inference: Approximate Bayesian Computation ABC; likelihood-free inference; Generative adversarial networks (GAN); applications in many fields including medicine, materials design, visualization, business, … 

Coordinator: Jukka Corander

3. Next generation data-efficient deep learning; including deep reinforcement learning.

Coordinator: Harri Valpola

4. Privacy-preserving and secure AI: Privacy-preserving machine learning; differential privacy; adversarial machine learning.

Coordinators: N. Asokan, Antti Honkela

5. Interactive AI: Interactive machine learning; probabilistic inference of cognitive models from data; probabilistic programming for behavioral sciences.

Coordinator: Antti Oulasvirta


Specific projects:

6. Topic: Constraint-Based Optimization and Machine Learning, Dr. Tomi Janhunen, Department of Computer Science, Aalto University

We are seeking for a postdoctoral researcher to work in the area of constraint-based optimization in order to solve challenging AI related problems. In particular, we are interested in the interconnection of constraint-based techniques and machine learning, either from the application perspective or potentially enhancing constraint-based systems with primitives emerging from machine learning. The candidates of interest have PhD in Computer Science, with a major subject relevant to computational logic such as knowledge representation and reasoning, constraint programming, Boolean modeling and optimization, answer set programming.  Moreover, we expect a track record on solving application problems using these techniques and/or developing related solver technology. Strong programming skills (such as C, C++, Python, ML, and Haskell) are considered as an asset.

7. Probabilistic Machine Learning, Professor Samuel Kaski, Department of Computer Science, Aalto University

I am looking for a postdoc or research fellow to join the Probabilistic Machine Learning group, to work on new probabilistic modelling methods and inference techniques. For this position I am open to excellent and/or exciting suggestions, especially around the themes of Approximate Bayesian Computation or Bayesian deep learning. Can be theoretical or applied work or both; the group has excellent opportunities for collaboration with top-notch partners in multiple applications. More information:

8. Probabilistic machine learning for personalized medicine, Professor Samuel Kaski, Department of Computer Science, Aalto University

I am looking for a postdoc who wants to participate in developing the new probabilistic modelling and machine learning methods needed for genomics-based precision medicine and predictive modelling based on clinical data. Suitable candidates have either a strong background in machine learning and a keen interest to work with top-level medical collaborators to solve these profound medical problems, or strong background in computational biology and medicine, and a keen interest to develop new solutions by working with the probabilistic modelling researchers of the group. More information: 

9. Probabilistic modeling and machine learning for bioinformatics, Assoc. Prof. Harri Lähdesmäki, Department of Computer Science, Aalto University

We are looking for a postdoc to develop probabilistic machine learning methods, including Gaussian processes, deep generative models and non-parametric longitudinal methods, with applications to bioinformatics. Applications include single-cell cancer immunotherapy and longitudinal multi-omics personalised medicine studies, both in collaboration with biomedical research groups. Applicants are expected to have strong background in probabilistic modeling, machine learning, programming, and have previous experience with (or desire to learn) bioinformatics and high-throughput data analysis. For more information and relevant recent publications, see ( or contact Harri Lähdesmäki (

10. Non-parametric probabilistic machine learning, Assoc. Prof. Harri Lähdesmäki, Department of Computer Science, Aalto University

We are looking for a postdoc and a PhD student to develop novel non-parametric and deep machine learning methods for time-series and structured data, including data-driven non-parametric ordinary and stochastic differential equations and non-stationary/deep Gaussian processes with sparse approximations and inference methods. Applicants are expected to have strong background in probabilistic modeling, machine learning, programming, and have previous experience with (or desire to learn) auto-differentiation/Stan/TensorFlow. For more information and relevant recent publications, see ( or contact Harri Lähdesmäki (

11. Bioinformatics and computational biology, Assoc. Prof. Harri Lähdesmäki, Department of Computer Science, Aalto University

We are looking for a postdoc to develop and apply advanced bioinformatics methods for high-throughput transcriptome, epigenome and single-cell data. Work is carried out in collaboration with molecular biology and biomedical research groups at University of Turku, University of Helsinki and international collaborators. Applications include immunology, cancer and personalised medicine. Applicants are expected to have strong background in bioinformatics, probabilistic modeling, high-throughput data analysis, and programming. For more information and relevant recent publications, see ( or contact Harri Lähdesmäki (

12. Computational HCI, Assoc. Prof. Antti Oulasvirta, Department of Communications and Networking, Aalto University

The User Interfaces group at Aalto University is looking for a postdoctoral scholar for exciting research topics at the intersection of computational sciences and human-computer interaction. The group is funded by a European Research Council (ERC) grant and consists of five postdocs, three PhD students, and two assistants. The research topics include fundamental aspects of computational design and interaction: model acquisition from data, simulation and cognitive models, optimization and machine learning methods, interactive support for designers, as well as demonstrators in key application of HCI. We invite applications from outstanding individuals with suitable background for example in Computer Science, Data Sciences, Human-Computer Interaction, Computational Statistics, Machine Learning, Information Visualization, Neurosciences, or Cognitive Science.

For more information and relevant recent publications, see Homepage of PI Antti Oulasvirta with example papers: and group homepage at

13. Privacy-preserving federated machine learning, Professor Samuel Kaski, Department of Computer Science, Aalto University

We develop methods for learning from data given the constraint that privacy of the data needs to be preserved. This problem can be formulated in terms of differential privacy, and we have introduced ways of learning effectively even under extremely distributed data, and for sharing data. A couple of “minor” problems still remain in this challenging field; come to solve them with us! More information:

14. Probabilistic user modelling in interactive human-in-the-loop machine learning, Professor Samuel Kaski, Department of Computer Science, Aalto University

Interactive human-in-the-loop machine learning combines the skills and knowledge of humans with the computational and processing strengths of machines. We are developing new approaches and applications for interactive human-in-the-loop machine learning using probabilistic modelling methods, with the aim of increasing the performance and efficiency of the systems and for improving the user experience. This project lies at the intersection of machine learning, human-computer interaction, and cognitive science. More information:


Doctoral students: Apply in the HICT call at and select your favourite FCAI project. Please note that the application deadline for doctoral students is 12.8.2018.

Postdoctoral positions: Choose in the application form one or more of the research programs and/or projects described above and explain in the motivation letter how you could contribute in the selected research area(s).

Required attachments:

  • A letter of motivation describing your previous research experience and future research interests linked with the FCAI research programs and/or chosen project(s). Maximum length: 1 page.

  • CV

  • List of publications

  • A transcript of the doctoral studies and degree certificate of the PhD degree

All material should be submitted in English. Short-listed candidates may be invited for an interview in Helsinki or via skype. Application is now closed.

Postdoctoral positions: we will start processing the applications on August 12th, 2018 so please apply quickly. The call will remain open until the positions are filled. By applying to this call, organized by the Finnish Center for Artificial Intelligence, you apply with one application to both Aalto University and the University of Helsinki. The employing university will be determined according to the location of the supervising professor.


Doctoral students: see instructions at

Postdoctoral positions: candidates should have a PhD in Computer Science, Statistics, Data Science or a related quantitative field and are 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 interests, and their relevance to FCAI research programs. Efficient and successful completion of studies is considered an additional merit.


Doctoral students:  see instructions at

Postdoctoral positions: The salary for a postdoctoral researcher starts typically from 3 500  EUR per month, and increases based on experience. In addition to the salary, the contract includes occupational health benefits, and Finland has a comprehensive social security system. The annual total workload at recruiting universities is 1 624 hours. The positions are located at Aalto University’s Otaniemi campus or University of Helsinki’s Kumpula campus.

The selected candidates 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, persons hired are expected to participate in the supervision of students and teaching following the standard practices of the hiring departments.


  • Research-related information: supervisor or coordinator listed above (

  • Application process: Akseli Kohtamaki (


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