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Dr Vladimir Eltsov investigates topological matter experimentally at ultra-low temperatures. In the future, such materials promise to change everyday life by making quantum devices available even at room temperatures, but currently there are still a lot of questions to answer, like resistance to the noise from the environment. Read more.

Professor Mario Di Francesco's project aims to overcome the limited resources of mobile devices for cross-reality - virtual, augmented, and mixed reality. It does so by moving heavy processing to remote servers, while guaranteeing high quality and low latency for a truly immersive user experience.

Professor Mathias Groth aims to elucidate the role of molecules and photons in achieving highly radiative edge plasmas in nuclear fusion reactors. The project will be carried out at 911������ in collaboration with scientists from Germany, the UK and the USA.

Professor Olli Ikkala investigates soft materials consisting of polymers, liquid crystals, proteins, surfactants, colloids, and biological matter, thus involving a wide range of functional properties, from adhesion, stretchability, and absorption properties to even "life-like" properties. Read more.

Assistant Professor Alex Jung and his colleagues develop modern AI-based methods for condition monitoring of powertrains. The results of the project are expected to produce new knowledge on how to optimally leverage AI algorithms for energy conversion systems.

Professor Fabricio Oliveira’s project will further develop the decision programming framework as a methodology for modelling and solving multi-stage decision problems under uncertainty. The outputs of this project offers researchers and practitioners a general modelling approach for addressing challenging decision problems, including those encountered in diagnostic testing in healthcare, selection of risk mitigation actions for safety-critical systems and cost-benefit analyses for climate change mitigation strategies.

Professor Vanni Noferini and collaborators have discovered a completely new class of functions called "centrality measures" that have the potential to overcome certain disadvantages that more classical measures are known to have. “Who is the most influential person among those you follow on Twitter?” or “Which reindeer should be isolated from the herd to avoid the spread of a deadly disease?” are examples of the questions that network theory can answer via centrality measures. This project will advance the study of these novel measures, and to apply them to problems in biology, economics, and finance.

Professor Patrick Rinke targets low cost, high efficiency solar cells. In the LearnSolar project, he will develop and apply a machine-learning based materials design approach to find more stable and environmentally friendly perovskites, a new and promising class of solar cell materials.

Assistant Professor Jukka Suomela’s research group studies which computational tasks can be solved rapidly with distributed algorithms, and which computational tasks are such that solving them with any distributed algorithm will necessarily take a long time. A theory of such systems will help us to understand not only man-made communication networks but also systems taking place in nature.

Assistant Professor Jara Uitto’s group studies frameworks that are designed to process big data. Their goal is to understand what can be computed by the aforementioned frameworks. This allows the researchers to show how to solve fundamental problems efficiently and identify provably hard problems. Ideally, this allows us to understand how the practical frameworks need to be adjusted to provide more efficient data processing, researchers explain in their project description.