The Helsinki Institute for Information Technology (HIIT) invites applications in the field of Artificial Intelligence. Artificial intelligence (AI) is without a doubt one of the most important factors in the development of modern digital societies. Our main research focus is the study of methodological issues in AI and their wide applications, involving statistical and/or symbolic problems or their combinations, with partner usage.

Transformative AI Collaboration

Aalto University has launched a new collaborative initiative in transformative research to support cooperation between AI and Data Science researchers with researchers in other fields. The aim is to create new knowledge by developing AI methods to advance research in other domains. Click here to view more information about the Transformative AI Collaboration projects.

Transformative AI Collaboration Projects

Finnish Center for Artificial Intelligence (FCAI)

Our affiliated unit, the Finnish Center for Artificial Intelligence (FCAI), is a hub for AI research within the Helsinki region. FCAI is an Academy of Finland flagship – an institute recognised for high-quality research with broad economic and societal impact. FCAI hosts a community of experts who seek to promote and further develop AI research. Click here to view more information about FCAI and project involved in this collaborative call.

Finnish Center for Artificial Intelligence (FCAI)

Members of our larger Helsinki ICT community also provide funding for researchers as Postdoctoral Researchers. Researchers in these positions are hired to work on self-funded projects or in externally funded research groups. More information about these self-funded projects and research groups are listed below.

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Supervisor

Project Information

Aki Vehtari (Aalto University)

Bayesian workflows for iterative model building and networks of models

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. The practical workflow recommendations and 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.

Sometimes the user wants also to build and compare models of different complexity or based on different assumptions.  Similar workflow ideas can be used to support also analysis of networks of models, making it easier to illustrate similarities and important differences. 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

Antti Honkela (University of Helsinki)

Privacy in machine learning for health

Differential privacy allows developing machine learning algorithms with strong privacy guarantees. In this project, we will study the privacy of state-of-the-art deep learning models used in health applications. We will seek to both evaluate the level of privacy provided by standard approaches, as well as apply differential privacy to improve the privacy. The project requires previous experience on machine learning and deep learning. Previous experience on differential privacy is an advantage but not required.

More information and papers: https://www.cs.helsinki.fi/u/ahonkela/ or by email (antti.honkela@helsinki.fi).

Arno Solin (Aalto University)

Learning with probabilistic principles

We are looking for exceptional and highly motivated post-doctoral researchers to work on applications and methods development in probabilistic machine learning. The general focus area is in development of inference methods and efficient learning for parametric and non-parametric models. The work relates to uncertainty quantification in deep learning, and contributes to the wider research goals of the research group (see http://arno.solin.fi).

Successful applicants are expected to have knowledge and experience in probabilistic methods and/or application areas of machine learning models such as computer vision or reinforcement learning. Furthermore, applicants are expected to have a track-record of publishing in relevant venues that reflect their expertise.

Eetu Mäkelä (University of Helsinki)

Bridging the gap between large heterogeneous data and trustworthy research in the social sciences and humanities (BRIDGE)

This project aims to develop more epistemologically sound research processes for data-centric research in the digital humanities and computational social sciences. Work in the project starts from three epistemic validity -threatening problems that the applicant has identified to plague much research in the domain. While different, these three all arise from the non-standard and heterogeneous nature of the large datasets of interest in such research. They are: 1) errors, gaps, bias and representativeness issues in the source data, 2) the abundance in the data of complex interactions and confounding factors and 3) a gap existing between what is in the data and the object of interest.

In the proposal, these problems are proposed to be tackled by a multi-pronged approach that will utilize approaches from multiple fields of computer science, statistics and beyond to unearth and correct for the issues identified: formal Bayesian modelling, other more informal means of capturing uncertainty, interactional support and process design, as well as algorithmic data-mining approaches such as clustering and anomaly detection

Giulio Jacucci (University of Helsinki)

Human Computer Interaction and AI

Area1: Intelligent User Interfaces, recommendation systems, user tracking and privacy

Area2: ehealth, wellbeing and behavior change, AI in patient journey, conversational agent for wellbeing

Area3: Multimodality, neuroadaptivity , affective interaction in XR , tangible and haptics

Harri Lähdesmäki (Aalto University)

Deep generative modeling for continuous-time dynamics and biomedicine

We are looking for postdoc to work on deep generative modeling for applications that are at the intersection of biomedicine and health as well as continuous-time dynamics. Our research work involves developing new deep generative modeling methods for large-scale health datasets from Finnish biobanks, computational immunology, various single-cell datasets, and general data-driven modeling methods for continuous-time dynamics. Methodologically, the work revolves primarily around conditional deep generative models, neural ODEs, and Bayesian methods. Work can focus more on method development or can also include applications, depending on applicant’s own preference. Applicants are expected to have good knowledge of machine/deep learning, statistics, programming, and (optionally) interest in bioinformatics and biomedicine. For more information, see our research group web page: https://research.cs.aalto.fi/csb/

Jörg Tiedemann (University of Helsinki)

HPLT – High Performance Language Technologies

HPLT is a new EU-Horizon project in collaboration with 5 European universities (Prague, Edinburgh, Oslo, Turku and Helsinki), 2 HPC centers (in Norway and the Czech Republic) and one Spanish LT company on the development of language and translation models at scale. We propose a language data space and sustainable procedures to lower barriers to train large and competitive NLP models. The project focuses on multilinguality, reproducibility and openness. We will use modern high-performance compute infrastructures for scalable integration of data, code and models and we will create frameworks that are at the forefront of AI with language data.

Kai Puolamäki (University of Helsinki)

Explainable and robust AI for scientists

Machine learning and AI are extensively used in the sciences. When modelling physical systems, understandability and statistical robustness of the models is often more important than predictive accuracy. We are looking for talented postdoctoral researchers and doctoral students to study explainable and understandable AI and to study the uncertainty quantification of the AI models. While the AI methods we develop are generic and not tied to any specific application domain we work closely with scientists to build Virtual Laboratory for Molecular Level Atmospheric Transformations. More information: https://bit.ly/edahelsinki2022jobs

Keywords: explainable AI, uncertainty quantification

Matti Järvisalo (University of Helsinki)

Constraint Reasoning and Optimization

The Constraint Reasoning and Optimization group led by Prof. Matti Järvisalo focuses on the development and analysis of declarative methods to solving computationally hard decision, search, optimization and counting problems with real-world relevance. The group has a strong track record in developing openly available state-of-the-art declarative systems ranking at the top in various solver competitions (MaxSAT Evaluation, Model Counting Competition, ICCMA argumentation competition, PACE, …) and high-quality research with various best papers awards (JAIR, SAT, CP, KR, ECAI, ICLP, …). We are looking for a postdoc with a proven track record in theoretical and/or practical aspects of declarative techniques (SAT, SMT, ASP, IP, CP, local search, etc) and their applications to work with us on forefront research questions related to our current research projects. There is ample room within the projects to align the research focus of the postdoc based on the expertise and interests of the candidate.

Nitin Sawhney (Aalto University)

Reconstructing Crisis Narratives research project

Join a collaborative team of PhDs, Postdocs and academic researchers working on a research project, Reconstructing Crisis Narratives for Trustworthy Communication and Cooperative Agency, supported by the Academy of Finland. The project, jointly conducted between Aalto University and the Finnish Institute for Health and Welfare (THL), is seeking to analyze and reconstruct crisis narratives using mixed-methods, combining qualitative research for narrative inquiry with computational data analytics of crisis discourses in news and social media to understand global pandemics. Candidates will work at the intersection of Human-Computer Interaction (HCI), design research, computational social sciences, and public health for critical societal impact. We expect the candidates to have backgrounds in computer science and/or the social sciences.

Potential duties and tasks may include the following:
– Automating content analysis for narrative work using suitable machine learning techniques for Natural Language Processing (NLP) such as Conversation Analysis, Content Classification, and/or Sentiment Analysis. This includes collecting and curating datasets, devising suitable methodologies, setting up the research infrastructure and tools, and a pipeline for data extraction, analysis and validation.

– Representing and visualizing crisis narratives to support understanding and collaborative sensemaking among key stakeholders and diverse publics. These tools should support browsing, searching, and exploring information for selected crisis themes and narratives. Work in this area includes not only developing prototypes of visualizations, but also conducting design research, user experience (UX) evaluation, and pilot assessment of such tools.

The candidate is not expected to master all these domains, but work closely with a multi-disciplinary research team to lead design and development efforts, while learning and contributing to ongoing work in specific research areas of interest.

Pekka Marttinen (Aalto University)

Machine Learning for Health (ML4H)

Accumulation of health data has enabled researchers to study questions such as: how to accurately predict the risk of disease, how to provide personalized treatment based on real-time patient data, or how to use genomic data to understand disease or antibiotic resistance. Central challenges in ML4H include noisy data, multiple heterogeneous data sources, learning about causality, interpreting the models, and assessing the uncertainty of predictions, to name a few. We tackle these by developing models and algorithms which leverage modern machine learning principles: Bayesian neural networks, deep latent variable models, Gaussian processes, causal inference, transformers, reinforcement learning, and natural language processing. Successful applicants are expected to have outstanding skills in machine learning, statistics, applied mathematics, or a related field, and the topic may be adjusted based on the applicant’s interests to either methodology or applications; examples of both can be found in https://users.ics.aalto.fi/~pemartti/.

Sampsa Hautaniemi (University of Helsinki)

Modeling and machine learning in precision oncology

Cross-disciplinary research group of Prof. Sampsa Hautaniemi aims at discovering causes for drug resistance in cancer patients and effective treatment options to overcome drug resistance using mathematical modeling and machine learning. We have strong track-record in modeling and computational method development (e.g., https://pubmed.ncbi.nlm.nih.gov/33720334/, https://pubmed.ncbi.nlm.nih.gov/35196078/ & https://pubmed.ncbi.nlm.nih.gov/29769198/)  as well as translating research results to clinical practice (e.g., https://ascopubs.org/doi/full/10.1200/PO.18.00343). We coordinate one of the largest ovarian cancer precision oncology projects in the world funded by EU Horizon 2020 (DECIDER; https://www.deciderproject.eu/). In DECIDER our focus is to discover effective treatment options for ovarian cancer patients who do not respond to chemotherapy anymore. More information and all our publications are available at our group website: https://www2.helsinki.fi/en/researchgroups/systems-biology-of-drug-resistance-in-cancer

We are looking for post-doctoral researchers with strong experience in mathematical or statistical modeling, machine learning and interest in using real-world cancer patient data in modeling and analysis. Basic knowledge of cancer biology or genetics is not required but is considered as a plus. Examples of the possible projects are: mathematical modeling tumor evolution during treatment using longitudinal data; causal analysis of single-cell data; development and use of Deep Learning methods in histopathological images/genomics data; multi-omics integration.

The Hautaniemi group belongs to the Research Program in Systems Oncology (ONCOSYS; https://www.helsinki.fi/en/faculty-medicine/research/research-programs-unit/systems-oncology-oncosys). ONCOSYS consists of internationally acclaimed basic, translational and clinical researchers who share the vision of using cutting-edge measurement technology, real-world data and AI in cancer research in order to discover effective diagnostic, prognostic and therapeutic approaches. If you enjoy doing science in an interdisciplinary and collaborative environment, consider applying to us.

Samuel Kaski (Aalto University)

Maximally Autonomous AI Assistant (MAMAA)

Current machine learning approaches are showing outstanding success in automating tasks. However, they require the goal to be specified precisely, for instance as rewards, and in real-world applications we often cannot do that. Then reinforcement learning based solutions will happily produce complex solutions which we do not want, for instance in robotics or drug development pipelines.

In this project, we develop inference methods for AI assistants which automate as much as possible but not more – they need to get more information from the human when necessary, to reduce their uncertainty about the goal and to acquire generalisable long-term decision-making skills through reinforcement learning to reach this goal.

We are looking for a postdoc knowledgable in probabilistic machine learning and reinforcement learning. Additional knowledge in any of the following will be helpful: game theory, multiagent systems,  computational rationality, inverse reinforcement learning.

This project is collaboration with Prof. Ville Kyrki (robotics), TU Delft, MIT, pharma and self-driving car companies

Samuel Kaski https://people.aalto.fi/samuel.kaski

Probabilistic Machine Learning https://research.cs.aalto.fi//pml/

Machine learning for drug design

Recent progress in machine learning for generative and predictive models of molecules has made computational drug design possible and created digital twins for the drug design Design-Make-Test-Analyze (DMTA) cycle, leaving as bottleneck the human skills needed for the Analyze step for deciding which molecule to make next. We  develop methods and models for cooperative AI assistants for the Analyze step to be able to help designers operate a virtual drug design laboratory. The principles of virtual laboratories, and cooperative AI-assistance in them, will be generalizable to other important research fields such as material science.

We are looking for a motivated researchers with background in computational sciences, machine learning, statistics. This is a joint research project between Aalto, Chalmers University and AstraZeneca. The researcher would join the  Probabilistic Machine Learning (PML) research group at Aalto, https://research.cs.aalto.fi//pml/

Supervision: Professor Samuel Kaski (Aalto), Dr Ola Engkvist (Chalmers University), Dr Markus Heinonen (Aalto)

Keywords: probabilistic modelling, human-in-the-loop modelling, drug design, deep learning