University of Nova Gorica spearheads major machine learning project
Nova Gorica, 24 February - The University of Nova Gorica has won EU funds for a five-year machine learning project called SMASH, as part of which 50 post-doctoral researchers will get fellowships to develop cutting-edge machine learning applications for science and humanities.
The project, which kicks off in July, is worth EUR 10 million, of which the EU will contribute EUR 5 million under the Horizon Europe Marie Sklodowska-Curie Actions, and the Ministry of Higher Education, Science and Innovation the remaining EUR 5 million.
This is the first COFUND-type project led by a Slovenian institution, University of Nova Gorica Rector Boštjan Golob told the press on Friday.
The funding will be used for basic research across five areas: machine learning for scientific applications, machine learning for particle physics, computing for human and animal communication, machine learning in climate research, and personalised medicine and life sciences.
Machine learning is widely used in artificial intelligence and involves training algorithms on vast troves of data so that they can make predictions without being explicitly programmed to do so.
The research will be conducted at five Slovenian institutions, including the University of Ljubljana and the Jožef Stefan Institute, and in collaboration with 32 international companies and institutions such as CERN in Geneva, Queen Mary University from London and the US university UC Berkeley.
The researchers will have access to Vega, Slovenia's largest supercomputer, which is operated by the Institute of Information Science, one of the partners on the project.
Matjaž Ličer from the Environment Agency, another project partner, said machine learning was indispensable both when there is a lot of data and when there is too little data. Planetary science, for example, is plagued by a lack of data.
Machine learning can also be used to recognise patterns that are not human language but are structurally similar to language, which opens new opportunities to detect structural patterns for example in animal communication, according to Artur Stepanov from the Cognitive Science Centre at the University of Nova Gorica.