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

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 August 9th, 2021 (midnight UTC+03:00). By applying to this call, organized by Helsinki Institute for Information Technology HIIT, 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.

Open positions

HIIT funded positions:

The mission of the Helsinki Institute for Information Technology HIIT is to enhance the quality, visibility and impact of Finnish research on information technology and support cooperation between ICT researchers, researchers in other fields, industry and public organisations. All excellent researchers in any area of ICT will be considered, but priority is given to candidates who support one (or more) of the strategic focus areas listed below.

  1. Artificial Intelligence. In this area our flagship unit is FCAI supporting its research programmes and highlights described above, but HIIT welcomes all excellent applications from areas of artificial intelligence research not necessarily covered by FCAI.
    Lead: Professor Samuel Kaski
  2. Data Science. Extraction of knowledge and insights from data is important across many fields of science. This research area is aligned with the Helsinki Centre for Data Science (HiDATA) aiming 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.
    Lead: Professor Sasu Tarkoma
  3. Cybersecurity. HIIT supports the research activities of the Helsinki-Aalto Institute for Cybersecurity (HAIC), a strategic initiative set up by Aalto University and the University of Helsinki to ensure excellence in information security research and education. The long-term mission of this research area 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.
    Lead: Professor Valtteri Niemi
  4. Computational Health. The goal of this research area is 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).
    Lead: Professor Juho Rousu
  5. Other research areas. List all areas of your expertise. HIIT has a long track record of supporting cross-disciplinary work, so feel free to include non-ICT areas of your expertise as well.

PI funded positions:

These projects are not directly funded by HIIT, but by individual research groups:

  1. Open post-doc position in Prof. Janne Lindqvist’s group – science of human-computer interaction
    Are you frustrated in the lack of science in the field of human-computer interaction (HCI)? We are looking for post-docs interested in transforming science in HCI. Background and interest in measuring user experience, modeling, human-computer interaction, computer science or social and behavioral sciences is required. Examples of work done in the group can be found at the website for the group https://www.lindqvistlab.org/. Please contact me at the aalto.fi email address about these positions during August 2021.
    Supervision:
    Prof. Janne Lindqvist https://www.aalto.fi/en/janne-lindqvist (Aalto University, Department of Computer Science, Director Helsinki-Aalto Institute for Cybersecurity (HAIC) HAIC.fi Vice-Director of HIIT hiit.fi).
  2. Open post-doc position in Prof. Janne Lindqvist’s group – multitasking and productivity tools
    We are looking for post-docs interested in understanding productivity tools and multitasking. Background and interest in measuring user experience, modeling, human-computer interaction, computer science or social and behavioral sciences is required. Examples of work done in the group can be found at the website for the group https://www.lindqvistlab.org/. Please contact me at the aalto.fi email address about these positions during August, 2021.
    Supervision:
    Prof. Janne Lindqvist https://www.aalto.fi/en/janne-lindqvist (Aalto University, Department of Computer Science, Director Helsinki-Aalto Institute for Cybersecurity (HAIC) HAIC.fi Vice-Director of HIIT hiit.fi).
  3. Open post-doc position in Prof. Janne Lindqvist’s group – mixed methods HCI and security research
    We are looking for post-docs interested in pushing the envelope in mixed methods HCI and security research. Background and interest in measuring either qualitative methods or quantitative methods, and interested to learning new methods, user experience, modeling, human-computer interaction, computer science or social and behavioral sciences is required. Examples of work done in the group can be found at the website for the group https://www.lindqvistlab.org/. Please contact me at the aalto.fi email address about these positions during August 2021.
    Supervision:
    Prof. Janne Lindqvist https://www.aalto.fi/en/janne-lindqvist (Aalto University, Department of Computer Science, Director Helsinki-Aalto Institute for Cybersecurity (HAIC) HAIC.fi Vice-Director of HIIT hiit.fi).
  4. HAIC: Open post-doc position in Prof. Janne Lindqvist’s group – security engineering and usable security
    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 topic will be agreed together with the applicant. Examples of work done in the group can be found at the website for the group https://www.lindqvistlab.org/. The post-docss will also get to participate in the activities of HAIC. Please contact me at the aalto.fi email address about these positions during August, 2021.
    Supervision: Prof. Janne Lindqvist https://www.aalto.fi/en/janne-lindqvist (Aalto University, Department of Computer Science, Director Helsinki-Aalto Institute for Cybersecurity (HAIC) HAIC.fi Vice-Director of HIIT hiit.fi).
  5. HAIC: Open post-doc position in Prof. Janne Lindqvist’s group – artificial intelligence and machine learning for human-computer interaction
    We are looking for post-docs interested in developing novel artificial intelligence and machine learning approaches to human-computer interaction.. Background and interest in data science, machine learning, statistics and computational approaches to computer science are required. Examples of work done in the group can be found at the old website for the group https://www.lindqvistlab.org/. The post-docs will also get to participate in the activities of HAIC. Please contact me at the aalto.fi email address about these positions during August, 2021.
    Supervisor: Prof. Janne Lindqvist https://www.aalto.fi/en/janne-lindqvist (Aalto University, Department of Computer Science, Director Helsinki-Aalto Institute for Cybersecurity (HAIC) HAIC.fi Vice-Director of HIIT hiit.fi).
  6. Machine learning in precision oncology
    Cross-disciplinary research group of Prof. Sampsa Hautaniemi focuses on discovering reasons for drug resistance in cancer patients and effective treatment options to overcome them. We have strong track-record in translating research results to clinical practice (e.g., https://ascopubs.org/doi/full/10.1200/PO.18.00343) and coordinate one of the largest ovarian cancer precision oncology projects in the world funded by EU Horizon 2020 (https://www.deciderproject.eu/). We are looking for motivated postdoctoral researchers with strong computational and analytical skills. Researchers with mathematical modeling expertise and basic knowledge of cancer biology & tumor evolution are especially encouraged to apply. Group website: https://www2.helsinki.fi/en/researchgroups/systems-biology-of-drug-resistance-in-cancer
    Supervisor: Sampsa Hautaniemi (University of Helsinki)
  7. Recommender systems for species distribution modeling
    This position is focused on developing new algorithmic approaches to predict relative abundances of species. The proposal is to use recommender systems techniques, considering places as users and species as items. The application case is an archaeological record of early human occupation sites in China, as part of a research project “Environments and energy use of early humans on the edge”, funded by the Academy of Finland. The position is for two years. The starting time is flexible.
    Supervisor: Indrė Žliobaitė (Associate professor) + possibly one or two co-supervisors from biosciences and geosciences
  8. Bayesian workflow for iterative model building
    Statistical analysis is critical when it comes to obtaining insights from data. Despite the practical success of iterative Bayesian statistical model building, it has been criticized to violate pure Bayesian theory and that we may end up with a different model had the data come out differently. In this project, we formalize and develop theory and diagnostics for iterative Bayesian model building. We show that when the iterative model building is done carefully, the difference to the theoretically optimal result is negligible.
    The practical diagnostics guide the modeller through the appropriate steps to ensure safe iterative model building, or indicate when the modeller is likely to be in the danger zone. By making applied scientific research and data analysis more reliable and reproducible, our understanding of the world and decision-making will be improved. Related paper https://arxiv.org/abs/2011.01808 and video https://www.youtube.com/watch?v=ppKpwtGy8KQ
    Supervisor: Aki Vehtari
  9. Variable selection with missing data with applications to genetics
    Our current ability to measure massive numbers of variables, for example, in biological sciences (millions of DNA variants), in personal health applications (thousands of time points in activity monitoring) or in customer behavior modeling (credit card usage history) call for efficient ways to separate the important variables from unimportant ones.
    We are looking for a Postdoc or a PhD student to work on realistic variable selection problems where variables have been measured incompletely and where we have access only to statistical summaries of the underlying data. Our previous work include the Bayesian approach implemented in the FINEMAP algorithm (www.finemap.me). In this project, our motivating example is the world’s largest study on migraine genetics where these new variable selection methods are needed to assess which genetic variants are biologically connected to the migraine susceptibility. The project includes both theoretical (statistical) and empirical (simulations and genetic data analysis) components.
    Supervisor: Matti Pirinen, Research group pages: https://www2.helsinki.fi/en/researchgroups/statistical-and-population-genetics
  10. Eco-evolutionary control theory and its applications to drug therapies
    Predictability and control of evolving populations is an emerging topic of high scientific interest and vast translational potential in applications such as vaccine and therapy design. This project will extend eco-evolutionary control theory to model free and multi-species contexts. For example, we will develop methods to find optimal control protocols for cell populations, find out what are the key determinants of controllability of a multi-species community, and apply the results to therapy optimisation. These projects are at the interface of microbial evolution, statistical physics, and information theory. A highly quantitative background and a burning interest in evolution are required.
    Keywords: stochastic optimal control, eco-evolutionary dynamics, evolutionary theory, drug resistance.[1] Lässig M, Mustonen V, Walczak AM (2017) Predicting evolution. Nat Ecol Evol 1(3):1–9.[2] Lässig M, Mustonen V, (2020) Eco-evolutionary control of pathogens. PNAS, 117 (33), 19694-19704
    Supervisor: Professor Ville Mustonen (University of Helsinki). This is a collaborative project with the Lässig Group at the University of Cologne.
  11. Machine learning for improved combinatorial cancer therapies
    Several positions are open in a project with the aim to develop new tools for finding better treatments for complex diseases such a cancer, by finding combinations of drugs that work better than the drugs in isolation, both in terms of the efficacy of the combination therapy in treating the disease and the side-effects. The project builds on a recent breakthrough of predicting drug combination responses using machine learning with very high accuracy (Julkunen et al., Nature Communications, 2020). The project will further develop and and apply advanced machine learning and optimisation tools to achieve the results. The positions are affiliated to a large multi-year research grant “Machine Learning in Systems Pharmacology” funded by Academy of Finland for 2021-2025.
    Julkunen, H., Cichonska, A., Gautam, P., Szedmak, S., Douat, J., Pahikkala, T., Aittokallio, T. and Rousu, J., 2020. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nature communications, 11(1), pp.1-11.
    Supervisors: Juho Rousu (Aalto University), Tero Aittokallio (FIMM), Antti Airola (University of Turku), Tapio Pahikkala (University of Turku)
  12. Open Postdoc position in Keijo Heljanko’s group – Massively parallel distributing computing with GPUs
    The Postdoc position is in the context of the Academy of Finland project “Design and Verification Methods for Massively Parallel Distributed Systems (DeVeMaPa)”. The main focus of the project is on the use of GPU computing to accelerate Big Data processing and the theoretical foundations of these systems. Use cases include Bioinformatics applications and Big Data analytics.
    The project will develop methodology for the design and verification of massively parallel heterogeneous computing. We need new methods to support the massive increase in the amount of parallelism at all levels of the hardware/software stack. Such massive increases in parallelism will make some currently used programming paradigms infeasible and thus new methods need to be devised to cope with industrial Big Data use cases. These methods must also be accompanied with solid theoretical foundations, allowing for the development of automated testing and verification tools that are required to validate the parallelization runtime software before production deployment. A key challenge is the need for seamless integration of heterogeneous computing with GPUs and hardware accelerators (e.g., neural network accelerators), how can they all be handled in a unified Big Data programming framework?
    Supervisor: Prof. Keijo Heljanko, Department of Computer Science, University of Helsinki and HiDATA Helsinki Centre for Data Science (https://www.helsinki.fi/en/helsinki-centre-for-data-science)
  13. Bayesian machine learning for sensing
    We use flexible probabilistic models, from Bayesian deep learning to Gaussian processes, for enhancing the capability of physical sensors. For example, we design simple sensors together with the computational models to create a combination where both elements best complement each other. We are working on example cases in low-cost ultrasonic sensing, acoustic levitation, spectral imaging on ordinary cameras, and adaptive optics for exoplanet discovery. We already have personnel and collaborators working on these cases, and are now looking for a postdoc to work on general machine learning research topics to support them. Your task would be to conduct fundamental research on machine learning (aiming for publications in ICML, NeurIPS, JMLR etc.) related to one of more of the following topics
    1. Efficient approximate inference (geometric MCMC and/or variational approximations) for flexible Bayesian models
    2. Hybrid methods combining strong domain-specific models with data-driven elements
    3. Unsupervised or weakly supervised methods for signal data, and techniques for coping with limited labels (domain adaptation, few-shot learning)
    4. Real-time control for active sensing using e.g. model-based reinforcement learning
    An ideal candidate has strong publication record in machine learning, statistics or signal processing, and wants to continue fundamental research on core algorithms and models. We offer the opportunity to do this for interesting cases with unique data. See https://www.helsinki.fi/en/researchgroups/multi-source-probabilistic-inference/open-positions for more details.
    Supervisor:
    Arto Klami
  14. Deep learning with differential equations
    Recent machine learning breakthroughs include black-box modeling methods for differential equations, such as Gaussian process ODEs [1] and neural ODEs. These methods can be viewed as “infinitely” deep learning methods where the traditional deep learning methods (implemented with a finite number of layers) are replaced with continuous-time differential equation system which, in turn, are parameterized by deep neural networks, thus implementing deep learning methods that are effectively “infinitely” deep. These methods can be applied to standard classification and regression tasks but they are particularly useful to learn arbitrary continuous-time dynamics from data without any prior knowledge. We are looking for a postdoc to join our current efforts in (i) developing efficient Bayesian methods for robust learning of infinitely deep models from data, (ii) developing neural ODEs to learn unknown/arbitrary dynamics of high-dimensional systems (e.g. in robotics, biology, physics or video applications) using a low-dimensional latent space representation, and (iii) further developing these methods for reinforcement learning.
    For more information, see https://research.cs.aalto.fi/csb/publications.shtml and our relevant recent work e.g.[1] http://proceedings.mlr.press/v80/heinonen18a.html[2] http://proceedings.mlr.press/v89/hegde19a.html[3] https://papers.nips.cc/paper/9497-ode2vae-deep-generative-second-order-odes-with-bayesian-neural-networks[4] https://openreview.net/forum?id=aUX5Plaq7Oy[5] https://arxiv.org/abs/2102.04764[6] http://arxiv.org/abs/2106.10905
    Supervisor: Prof. Harri Lähdesmäki (harri.lahdesmaki@aalto.fi)
  15. Deep generative modeling for precision medicine and future clinical trials
    We are looking for two postdocs to develop probabilistic machine learning methods for heterogeneous health datasets from large-scale biobanks and clinical trials. This project involves developing novel deep generative modeling methods to 1) predict adverse drug effects using longitudinal / time-series data from large-scale clinical trials, and to 2) harmonize large-scale health data sets for AI-assisted decision making to revolutionize future clinical trial. Methodologically this project includes various deep generative modeling methods, such as variational auto-encoders and GANs, Bayesian (deep) neural networks, domain adaptation, and Gaussian processes. The work will be done in collaboration with several research groups from the Finnish Center for Artificial Intelligence, and the novel methods will be tested using unique and exciting real-world data from our collaborators in university hospitals and a big pharma company. For more information, see https://research.cs.aalto.fi/csb/publications.shtml and https://research.cs.aalto.fi/pml/publications.shtml and our relevant recent work e.g.[1] http://proceedings.mlr.press/v130/ramchandran21b.html[2] http://proceedings.mlr.press/v130/ramchandran21a.html[3] https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab021/6104850
    Supervisor(s): Profs. Harri Lähdesmäki (harri.lahdesmaki@aalto.fi), Aki Vehtari, Samuel Kaski
  16. Probabilistic modelling and Bayesian machine learning
    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 have exciting research topics available around the following areas, and I am also open to new suggestions: (1) simulator-based inference, for combining first-principles models with learning from data, (2) Bayesian deep learning, (3) Bayesian reinforcement learning and inverse reinforcement learning, (4) with multi-agent modelling, and (5) privacy-preserving machine learning and synthetic data generation. Can be theoretical or applied work or both; the group has excellent opportunities for collaboration with top-notch partners in multiple applications, including user interaction, health, genomics, and neuroscience. Links: http://research.cs.aalto.fi/pml
    Supervisor: Samuel Kaski (samuel.kaski@aalto.fi)
  17. Physics-inspired geometric deep representation learning for drug design
    We invite applications for a postdoc and doctoral student position in geometric deep learning aimed at advancing state of the art in drug design. Key research directions include new 3D generative models for molecules that reflect their underlying physical-chemical processes and dynamics, spatial constraints, local invariances and equivariances, and energy considerations; domain generalization in both structural and sequence spaces; deep geometric models for inducing diversity in molecular generation; learning with limited training data; and non-autoregressive inference methods. The work will augment and consolidate existing well-developed research pipelines of the supervisors. The selected student may also have the opportunity to work with and visit our collaborators at leading pharma companies and academic groups in Europe, USA, and Canada. Facility in implementing deep learning models is expected, and training in one or more of the following would be a plus: Statistical Mechanics, Bayesian Learning, Graph Neural Networks, Generative Models, Ordinary and Partial Differential Equations, and Computational Biochemistry. Some representative publications are given below.[1] John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola. Generative Models for Graph-Based Protein Design. NeurIPS (2019).[2] Vikas Garg, Stefanie Jegelka, Tommi Jaakkola. Generalization and Representational Limits of Graph Neural Networks. ICML (2020).[3] Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki. ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. NeurIPS (2019)[4] Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski. Deep learning with differential Gaussian process flows. AISTATS (2019), notable paper award (top 1%)[5] Kyle Barlow, Shane O Conchuir, Samuel Thompson, Pooja Suresh, James Lucas, Markus Heinonen, Tanja Kortemme. Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity Upon Mutation. Journal of Physical Chemistry B, 122(21):5389-5399 (2018)
    Supervisors: Prof. Vikas Garg, Prof. Samuel Kaski, PhD Markus Heinonen
  18. Probabilistic modelling for collaborative human-in-the-loop design
    We are looking for a postdoc and doctoral student interested in developing probabilistic modelling and inference methods needed for complex design tasks, with drug design as a case study. The idea is to help experts steer the modelling system towards their design goals, while eliciting their prior knowledge to improve the models of the drugs. This is difficult because the goals may be tacit, uncertain and evolving. Another postdoc will develop the new molecular models for drugs, to which you are welcome to contribute. The work will augment and consolidate existing well-developed research pipelines of the supervisors. The selected student may also have the opportunity to work with and visit our collaborators at leading pharma companies and academic groups in Europe, USA, and Canada. This will be a transformative project, resulting in a virtual drug design laboratory. Key methods we will need: probabilistic modelling and Bayesian inference, multi-agent modelling, sequential experimental design, POMDPs, reinforcement learning and inverse reinforcement learning. We expect applicants to master some of these, or be exceptionally eager and quick learners.[1] Celikok et al. Teaching to Learn: Sequential Teaching of Agents with Inner State. arXiv:2009.06227[2] Mikkola et al. Projective Preferential Bayesian Optimization. ICML 2020[3] Peltola et al. Machine Teaching of Active Sequential Learners. NeurIPS 2019[4] Kangasrääsiö et al. Parameter inference for computational cognitive models with Approximate Bayesian Computation. Cognitive Science 43 (2019): e12738.
    Supervisors: Prof. Samuel Kaski, Prof. Vikas Garg, PhD Markus Heinonen
  19. Machine Learning for Health (ML4H)
    Recent years have witnessed accumulation of massive amounts of health related data, enabling researchers to address a range questions 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 integrating noisy data from multiple heterogeneous data sources, going beyond correlation to learn about causality, interpreting the models, and assessing the uncertainty of predictions, to name a few. We tackle these by developing new models and algorithms which leverage modern machine learning principles: Bayesian neural networks, deep latent variable models, interactive machine learning, attention, reinforcement learning, and natural language processing. 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.
    Supervisor: Prof. Pekka Marttinen (pekka.marttinen@aalto.fi)
  20. Statistical Genetics and Machine Learning (INTERVENE)
    The emergence of genomic sequencing and other molecular profiling methods together with the digitalization of health care data collections have created unprecedented opportunities for better treatment options. INTERVENE is an international and interdisciplinary consortium that seeks to leverage these vast, but underused data resources to generate clinically actionable knowledge for improved understanding of diseases and treatment options tailored to individuals, by analyzing altogether 1.7 million sequenced genomes with longitudinal clinical data. We are looking for an outstanding postdoctoral researcher to join the interdisciplinary and international team of researchers in INTERVENE. From an applicant we expect a PhD in statistical genetics or a related field, or a PhD in a methodological field (e.g., machine learning, applied mathematics, statistics) coupled with a strong interest to apply the skills in the genetics application. More information about the project: https://www.interveneproject.eu/.
    Supervisor: Prof. Pekka Marttinen (pekka.marttinen@aalto.fi)
  21. Bayesian machine learning and differential privacy
    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. We focus especially on Bayesian methods, that enable quantifying the additional uncertainty due to noise injected to guarantee privacy (e.g. Kulkarni et al., ICML 2021) as well as convenient integration of prior knowledge when generating strongly anonymised differentially private synthetic data, but work on more general differentially private machine learning as well. Depending on the background and interest of the applicant, it is possible to focus on theory or practice. More information and papers: https://www.cs.helsinki.fi/u/ahonkela/ or by email (antti.honkela@helsinki.fi).
    Supervisor: Associate Professor Antti Honkela
  22. Methods for large scale fault-tolerant quantum computingLooking for scientists to research and develop scalable methods and software necessary at different layers of the quantum software stack of fault-tolerant quantum computers. The research topics range from decoders for quantum error-correcting codes, compilers for large scale fault-tolerant quantum circuits and verification methods of optimised circuits. For an architectural perspective please refer to https://doi.ieeecomputersociety.org/10.1109/MC.2020.2997277 The postdoc position is for two years. The candidate is expected to show a strong motivation and commitment to research by contributing to the realization of the tools that are developed at the department. Requirements: (1) PhD in Computer Science/Engineering or Physics, (2) Excellent programming skills in C++ and Python, (3) Preferable, experience with one of the following: Qiskit, Cirq, PennyLane, (4) Ability to work in a research team and (5) Good skills in preparation of research manuscripts.
    Supervisor: Prof. Alexandru Paler (alexandru.paler@aalto.fi)

FCAI Projects

Finnish Center for Artificial Intelligence FCAI is a community of experts that brings together top talents in academia, industry and public sector to solve real-life problems using both existing and novel AI. FCAI’s research mission is to create new types of AI that is data efficient, trustworthy, and understandable. We work towards these three scientific objectives by creating new techniques for AI-assisted decision-making, design and modeling. We develop AI techniques needed for systems which can help their users make better decisions and design better solutions across a range of tasks from personalized medicine to materials design. A core insight in developing such AIs is that they need to have world models for understanding the world and interacting with it, and user models for understanding the user and interacting with them.

  1. AI-assisted design and decisions: from foundations to practiceWe are making a coordinated effort to provide solutions for supporting human-AI collaboration in difficult design and decision tasks, selecting AI-assisted decision-making, design and modeling as a key highlight of FCAI. We are working on several parallel case studies ranging from computational physics to drug design and mobility (see topic H23).
    The principles and computational methods are largely shared across the cases, and we are hence looking for people interested in the grand challenge of building general-purpose tools for this. You will work together with the teams developing the example cases, so that your research will be on the fundamentals of AI assistance: You will identify commonalities to build the foundational principles, work on better computational methods for addressing recurring challenges without getting tangled with the messy details of any specific application, and in particular will work towards a concrete software environment that supports several example cases. The long-term goal is to make it easy for others to build their own AI-assisted technologies.
    We are looking for people with different kinds of profiles, with demonstrated excellence and strong desire to contribute to (a) the foundational basis of AI-assistance, (b) the required probabilistic machine learning techniques, and/or (c) open-source scientific software.
    We are looking for two research fellows, postdocs or senior software engineers for this topic.
    Supervision:
    Profs. Arto Klami (UH), Samuel Kaski (Aalto) with potential other supervisors
    Keywords:
    AI-assisted design, design of experiments, open software, probabilistic ML
  2. AI-assisted modelingModeling is a combination of a design task of building the model, and the statistical task of fitting the model to data or more generally statistical inference. While probabilistic programming is progressing fast on the latter part, and AutoML helps when there is enough data, much less help exists for the design task. We formulate the task of offering design help as a broader modeling task, which includes the modeller in the loop. The solution of the broader task gives both the AI-assistance and a solution of the primary task.This is a particular case of the more general AI-assisted design principle (see topic F1), and you will work towards a concrete demonstration of the principle for the case of statistical modeling. You will also contribute to the concurrent research on principles and software support for general-purpose AI-assisted design tools, together with people working on other example cases.
    We are looking for research fellows and postdocs for this topic.
    Supervision:
    Profs. Aki Vehtari (Aalto), Arto Klami (UH), with potential other supervisors
    Keywords: Probabilistic programming, Bayesian modeling, interactive modeling
  3. AI-assisted design of experiments and interventionsWe develop probabilistic modeling and inference techniques for designing the design-build-test-learn cycles which are ubiquitous in engineering systems, and experimental design in sciences and medicine. This needs Bayesian experimental design techniques able to work well with both simulators, measurement data and humans in the loop, who are both information sources and the final decision makers. Furthermore, we need automatic design of interventions (actions) for learning causal models partially from a combination of observational and interventional data.We are looking for a probabilistic modeling researcher interested in developing the new methods, with options on applying the techniques to improve modeling in highlight applications of FCAI, and synthetic biology, interface design and medicine.
    This is an instance of the tools necessary for AI-assisted design and decision-making (see topic F1), which are meant to generalize across domain-specific virtual laboratories launched at FCAI.
    We are looking for research fellows and postdocs for this topic.
    Supervision:
    Prof. Samuel Kaski (Aalto), with multiple other FCAI professors
    Keywords: Sequential design of experiments, Bayesian experimental design, likelihood-free inference, implicit models, causal inference
  4. Advanced user modelsTo advance cooperative forms of AI, we need AI methods that can accurately estimate what users prefer or think given observational data and, moreover, predict how they might experience an intervention the AI might take. At FCAI, we are working on new theoretical foundations of cooperative AI, which we believe must combine machine learning with theory-bound assumptions about human behavior. We seek to develop interactive agents that, on the one hand, can accurately reason about human behavior from observation only, and take action in applications such as AI-assisted design or decision-making.
    Several research groups at FCAI contribute to this work. We have several collaborative efforts at the intersection of machine learning and computational cognitive sciences, most notably on computational rationality (for example, see [1,2]). Computational rationality combines assumptions about bounded rationality with models of human cognitive architectures.
    A successful candidate is either a computational cognitive scientist with interest in machine learning methods (Bayesian inference, deep learning, reinforcement learning), or a machine learning researcher with a record of applications in user modeling.
    We are looking for research fellows and postdocs for this topic.[1] Kangasraasio et al. Cognitive Science 2019, https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.12738[2] Jokinen et al. CHI’21: https://dl.acm.org/doi/abs/10.1145/3411764.3445483
    Supervision:
    Profs. Antti Oulasvirta (Aalto), Samuel Kaski (Aalto), Perttu Hämäläinen (Aalto), Andrew Howes (Birmingham University; presently a visiting professor at FCAI)
    Keywords:
    User models, computational cognitive models, cooperative AI, computational rationality
  5. Virtual atmospheric laboratoryWe are looking for a postdoctoral researcher to join us in building Virtual Atmospheric Laboratory (VATLAB). The purpose of the VATLAB is to help us efficiently model atmospheric processes and to understand the underlying processes and causal connections. VATLAB will combine first-principles quantum chemical and other simulations, probabilistic machine learning (ML) models, and interactive visualizations. In this project, the hired person will work together with atmospheric scientists on the specific problem of atmospheric cluster formation, but the developed computational methods will be applicable in other domains too. The topics involve research in probabilistic modeling and interaction methods to explore the data and to build the ML models.
    The applicant will work in a multidisciplinary team involving both computer scientists and atmospheric scientists. A successful applicant should have knowledge of ML and related mathematics and knowledge of natural sciences, but specific prior knowledge of atmospheric clustering processes is not required. We will consider applicants with backgrounds related to fields in computer or atmospheric sciences.
    The project will be done in collaboration with the Department of Computer Science and Institute for Atmospheric and Earth System Research at the University of Helsinki. The project is part of the Academy of Finland funded Finnish Center for Artificial Intelligence (FCAI) and Atmosphere and Climate Competence Center (ACCC) flagships.
    We are looking for postdoctoral researchers for this topic (exceptional doctoral candidates can also be considered).
    Supervision:
    Profs. Kai Puolamäki (UH), Hanna Vehkamäki (UH); Dr. Theo Kurtén (UH), with potential other supervisors
    Keywords:
    Machine learning, interactive model building, probabilistic modeling, simulators, atmospheric sciences
  6. Multi-level simulation for sustainable autonomyTo study future sustainable mobility, FCAI is building Sustainable Autonomous Mobility Virtual Laboratory. The virtual laboratory will allow studying effects of autonomous traffic starting from control of individual vehicles, to their environmental effects such as pollution and noise, as well as their socio-economic effects. The virtual laboratory will integrate several simulators including an autonomous vehicle simulator, as well as other simulators modeling relevant phenomena.
    A central challenge in the integration is the exchange of information between the individual simulation models with different parameterizations. We approach this as an AI challenge where parameters of all simulators are inferred jointly from pools of data for each model. This will require efficient data-driven inference procedures.
    We are looking for research fellows and postdocs for this topic.
    Supervision:
    Profs. Laura Ruotsalainen (UH), Ville Kyrki (Aalto), with potential other supervisors
    Keywords:
    Multi-level simulation, sustainability, autonomous vehicles, simulator-based inference
  7. Closing simulation-real world gapTo study future sustainable mobility, FCAI is building Sustainable Autonomous Mobility Virtual Laboratory. The virtual laboratory will allow studying effects of autonomous traffic starting from control of individual vehicles, to their environmental effects such as pollution and noise, as well as their socio-economic effects. The virtual laboratory will integrate several simulators including an autonomous vehicle simulator, as well as other simulators modeling relevant phenomena.
    When relying on simulation models for data-driven analytics, a central issue is the reality gap, the difference between a simulation model and the real world. In practice, simulation parameters need to be inferred often from scarce real-world data. However, in addition to this calibration problem, the simulation model is unlikely to capture all real-world phenomena. Thus, addressing the sim-to-real problem requires also determining this residual gap in order to compensate for it. This problem requires data-efficient probabilistic methods that are simultaneously expressive.
    We are looking for research fellows and postdocs for this topic.
    Supervision:
    Profs. Ville Kyrki (Aalto), Laura Ruotsalainen (UH), with potential other supervisors
    Keywords:
    Sim-to-real problem, reality gap, data efficiency, autonomous mobility
  8. FCAI Research Programs and Highlight Programs: open applicationBesides the specific positions described above, you can send us an open application to apply for our Research Programs (R1–R6) and Highlight Programs (HA–HE). We are looking for postdocs and research fellows to contribute to fundamental research on Agile probabilistic modeling (R1), Simulator-based inference (R2), Deep learning (R3), Privacy and security (R4), Interactive AI (R5), and Autonomous AI (R6), and highlight applications including Modeling tools, Health, Atmospheric AI, and Materials science (please note that we are not recruiting new researchers to programs R7 and HC in this call). You can read more about our research here: https://fcai.fi/research.
    Please include a statement of your research interests in your cover letter, and specify which Research and/or Highlight Programs you are interested in contributing to. The candidates who proceed in the review will be later matched to more specific FCAI topics and supervisors.

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:

  • Cover letter which includes your motivation for applying for the specific position(s), how you could contribute to them, and other relevant information (no more than 2 pages). In case you have selected topic “5. Other research areas”, please describe all areas of your expertise. HIIT has a long track record of supporting cross-disciplinary work, so feel free to include also non-ICT areas of your expertise that may be useful in multidisciplinary collaboration.
  • CV
  • List of publications (please do not attach full copies of publications)
  • A transcript of doctoral studies and the degree certificate of the doctoral 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 Zoom.

Qualifications

The candidate should have a PhD and is expected to have an excellent track record in scientific research in one or more 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 3500 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 1624 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 https://www.aalto.fi/en/.

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 https://www.helsinki.fi.

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 https://www.hiit.fi/.

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 https://fcai.fi/.

Further information

  • Research related questions: The leader of the research center/program/project specified above
  • Application process and practicalities: Sanni Kirmanen (firstname.lastname@aalto.fi)

Apply