Academy Project Funding granted to internationally high-quality projects in natural sciences and engineering

As many as 46% of all Academy Project applications addressed to the Research Council were considered excellent (total rating 6 or 5) by the international reviewers. Most of the projects that received the highest rating (6) were funded, but only 13% of those that received the second highest rating (5) could be funded.

Of the highly rated applications, 18% were from early-career researchers. In turn, early-career researchers accounted for 34% of the subprojects selected for funding.The Research Council funded subprojects of early-career researchers by a total of 16.5 million euros, of which 9.25 million euros came from funds especially earmarked for early-career researchers.An applicant was considered to be an early-career researcher if no more than ten years had passed since their first doctorate.

Academy Project Funding is among the Research Council’s most important funding instruments for promoting the quality, impact and renewal of research.Professor Reko Leino, Chair of the Research Council for Natural Sciences and Engineering, said: “This year, many of the projects presented bold new scientific ideas.In addition, we received a number of high-quality, multidisciplinary consortium applications formed by several research organisations where the added value of the consortium was significant.”

In the funding decisions, the Research Council especially emphasised high scientific quality, feasibility, novelty and breakthrough potential.The aim was to fund research in a wide range of different fields and to take into account the special characteristics of each field.

Examples of funded projects

Anouar Belahcen and Alex Jung from Aalto University, Janne Keränen from VTT Technical Research Centre of Finland Ltd and Tatiana Minav from Tampere University have formed a consortium to develop modern AI-based methods for condition monitoring of electromechanical energy conversion systems, or powertrains.In order to ensure the safe and efficient functioning of these powertrains, it is important to predict their incipient faults at an early stage.The project will produce synthetic augmented data to be used to train the AI algorithms.The algorithms will also combine data from different application domains, allowing for transfer learning.The results of the project are expected to produce new knowledge on how to optimally leverage AI algorithms for energy conversion systems.

Kari Eskola from the University of Jyväskylä aims to develop a simultaneous theory-study of various observables in ultrarelativistic nuclear collisions in relation to the Quantum Chromodynamics (QCD) sector in the standard model of particle physics.The project aims to determine properties of the Quark-Gluon Plasma (QGP) from data produced at CERN’s Large Hadron Collider and the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory (BNL).Different computational methods will be used in the modelling, covering the whole nuclear collision.The project involves researcher training at all levels and is very timely in terms of the ongoing LHC heavy-ion experiments.

Antero Kukko from the Finnish Geospatial Research Institute (FGI) of the National Land Survey of Finland and Mikko Vastaranta from the University of Eastern Finland intend to develop a method to measure wood density without destructive means.The ability to measure wood density is fundamental to forest assessment and forest genetic improvement programmes, and to understand all uses of wood, including carbon capture.The most relevant factors explaining variations in density are related to growth: thickening of the stem, height growth and structural development of the canopy.In their project, Kukko and Vastaranta will utilise a detailed laser-scanning time series collected from 37 sample plots.The time series will facilitate detailed analysis and modelling of changes in the stem form, height growth and canopy development over seasons.

Clare Strachan from the University of Helsinki aims to develop a method to predict the response of cancer patients to chemotherapy medicines before the start of treatment.This involves a technique called Raman spectroscopy, in which the interaction of laser light with cancer cells and tissue is analysed.The interaction is used to detect molecular signatures that indicate if a patient will benefit from specific chemotherapy medicines.The method could improve the personalised optimisation of chemotherapy treatment and lead to improved patient outcomes.

Inquiries and more information

• List of funded projects
• Funding criteria and policies of the Research Council for Natural Sciences and Engineering
• Jenni Heino, Science Adviser, tel. +358 295 335 030, firstname.lastname(at)aka.fi
• Ulrika Jakobsson, Science Adviser, tel. +358 295 335 038, firstname.lastname(at)aka.fi
• Timo Lehtinen, Science Adviser, tel. +358 295 335 054, firstname.lastname(at)aka.fi
• Katrine Mahlamäki, Science Adviser, tel. +358 295 335 062, firstname.lastname(at)aka.fi
• Oskari Miettinen, Science Adviser, tel. +358 295 335 053, firstname.lastname(at)aka.fi

Academy of Finland Communications
Vesa Varpula, Communications Specialist
tel. +358 295 335 131
firstname.lastname(at)aka.fi