Transformative AI Collaborations

Transformative AI Collaborations2022-06-23T10:36:28+03:00

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.

We are especially seeking candidates interested in collaborations between Artificial Intelligence and/or Data Science with the following areas for Transformative AI collaborations:


  1.     Artificial Intelligence in economics

Research in the Economics Department has several connections to AI and computer science. On the empirical side, research makes use of large data sets and advanced computational methods varying from field experiments to structural econometric models. Topics of current interest include but are not limited to the green transition in both electricity markets and transport; education and its impacts, ranging from preschool to tertiary education; development economics; urban economics themes such as public transport and urban planning in general; pharmaceutical markets; public procurement auctions and innovation. On the theoretical side the department has a strong tradition in game theoretic modeling of various phenomena and institutions. Research questions center often around the implications of incomplete information and dynamics. The requisite skills vary on the project of interest, from strong applied AI-skills to the ability to work with mathematical models.

Collaborating supervisor: Otto Toivanen, Department of Economics (, School of Business

  1.     Automatic generation of production planning and scheduling optimization models from data

We aim at creating mathematical programming models by fitting a black box neural network model to the data collected from real or simulated processes and converting it into a “classical” (MILP, NLP) optimization model.

The approach would have several benefits:

1) Semi-automation in model generation
2) Automatic model calibration – represents reality, at least on average
3) Ready-made general solvers can be used for solving

Keywords related to the current approach include: Explainable NNs, metamodeling, gray-box modeling

Collaborating supervisor: Esko Niemi, Digital Production Group (, School of Engineering

  1.     AI and Digital Twins in Advanced Manufacturing

The physical manufacturing processes are supplemented by their Digital Twins.  Digital Twin is a concept that includes all the analytical capability and the data generated by the physical process in real time.  It is like weather forecasting for optimal manufacturing processes.  For the most optimal process we want to add sensors and other data gathering concepts for optimization.  The most of manufacturing process optimization with Digital Twins is based on analytical understanding of the process.  However, the amount of data that will be available will probably be beyond the capability of analytical models and this calls for new AI-based models.

Collaborating supervisor: Jouni Partanen, Additive Manufacturing Technology and Applications Group (, School of Engineering

  1.     Deep learning and process-based model fusion for improved understanding of the earth system

Earth system models are key to understanding the ongoing global change and potential mitigation strategies. Enhancing the capacity of the models underlying the predictions about the future conditions of our planet is crucial to mitigate and adapt to ongoing global environmental change. Towards this goal, deep learning methods have recently been applied also in the field of earth system modeling, contesting, or even surpassing the performance of traditional process-based models.

These two methodologies approach the modeling problems from different perspectives; deep learning and other machine learning methods are data-driven, limited by data availability, whereas process-based models are mainly limited by model flexibility and our understanding of the underlying processes.

This project focuses on understanding how to combine the strengths of these two types of models. Potential avenues to explore include, among others, 1) deep-learned model ensembles where deep learning informs an optimal model ensemble based on local conditions, 2) ensembles consisting of both data-driven and process-based models, 3) hybrid models integrating different aspects of DL and process-based models, or 4) identifying conditions where the strengths of each model type, data-driven or process-based, could be exploited. The desired outcome of this project is methodological advancements on how deep learning improves earth system modeling and practical applications leveraging the strengths of deep learning and process-based modeling in environmental and earth system sciences.

Collaborating supervisors: Matti Kummu, Olli Varis, Water and Development Research Group (, School of Engineering

  1.     Multilevel optimization of autonomously interacting textiles

In the Multifunctional Materials Design group, we target a paradigm shift for smart soft materials into user-customizable textiles that autonomously interact with their environment and communicate through changes in color or shape. This will be done by coupling modular thermo- and photo-actuators to different textile constructions ranging from traditional fiber arts to industrial knitting and weaving techniques. We work in parallel at two different hierarchical levels of implementing new functionalities into yarns and optimizing responses of different textile architectures composed out of these yarns. This opens an interesting question for data scientists of how to tackle open-ended research questions of combining multiple functionalities into one working textile. Another intriguing interface is how to combine qualitative – tactile sensing observations into the methods of data science/artificial intelligence. We are a welcoming group of researchers that embraces transdisciplinary methods and diverse – often surprising contributions (Multifunctional Materials Design | Aalto University).

Collaborating supervisor: Jaana Vapaavuori, Multifunctional Materials Design Group (, School of Chemical Engineering

  1.     Electric Drives

The School of Electrical Engineering offers several opportunities for collaborations with artificial intelligence research. These include research on control of electric drives and power electronic converters, including modeling nonlinear components and load-bearing components attached to electric drives. There are also opportunities in the research on the level of energy systems containing renewable energy production sources, leading to new demands for control technology of the power grid.

Collaborating supervisor: Marko Hinkkanen, Electric Drives Group (, School of Electrical Engineering

  1.     Biomaterials

The School of Chemical Engineering and School of Science host jointly the LIBER Centre of Excellence ( with multiple research directions having potential for transformative research in collaboration with artificial intelligence. The focus is on processes leading to emergent materials structures, complex functions of materials, and dissipative nonequilibrium processes. Functions that are developed are for example self-organization, regeneration, adaptability, and self-repair.  Other possible research directions include working with new imaging and data acquisition methods, and new computationally-aided design methods of new materials.

Collaborating supervisor: Markus Linder, LIBER Centre of Excellence (, School of Chemical Engineering

How can I apply?

In order to find the best match between candidates and supervisors, all applicants need to describe their research interests and how they see themselves contributing to transformative AI collaborative research.

The applications are to be submitted through the eRecruitment system. By applying through our eRecruitment system, you can apply to multiple focus areas. In the application, you are required to list the focus area you would like to apply to. Note: HIIT, FCAI, and the House of AI are using the same eRecruitment form. If you wish to apply to positions available at FCAI or at HIIT, please mark the appropriate tick box.

Required attachments:

(1) Cover letter (1-2 pages in length) which includes the itended research area and how you intend to contribute to the focus area. If you have a potential supervisor(s), you should clearly state your motivation for applying to work with said supervisor, how you could contribute to the research produced by the supervisor and research area, and other relevant information.

(2) CV

(3) List of publications (please do not attach full copies of publications)

(4) A transcript of doctoral studies and the degree certificate of the doctoral degree

(5) Contact details of possible referees from 2-3 senior academic people. We will contact your referees, if recommendation letters are required.

All materials should be submitted in English in a PDF format. Note: files should be 5MB max. You can upload up to five files to the eRecruitment system, each 5MB.

In the evaluation of the candidates, emphasis is put on the quality of the candidate’s previous research and international experience as well as the innovativeness, impact potential, feasibility of the research plan, and its relevance to one or more of the focus areas. The ability to link work between two or several research groups from different areas, and initiate or enhance collaboration, is an additional positive aspect.

Candidates short-listed for the position may be invited for an interview at the Otaniemi campus or for an interview conducted via Zoom.

Apply now