European Consortium for Mathematics in Industry
We are happy to announce that the MSCA-ITN-EID project BIGMATH – “Big Data challenges for Mathematics” – has been recently funded by the H2020 program. The project is coordinated by Università degli Studi di Milano and was born from a collaboration with other 3 ECMI academic centres (University of Novi Sad, TU-Eindhoven, IST Lisbon) and […]
This report refers to a project which has been assigned to a group of students in the MSc in Mathematics at Università degli Studi di Milano during a modeling seminar, in the academic year 2017/18. They faced the following problem. A group of biochemists in the University of Milan conducted an experiment testing the effect […]
A recent reform has redesigned Common Agricultural Policy (CAP) contents over the programming period 2015-2020, introducing important changes in the funding strategies, called “Greening”, which reward “agricultural practices beneficial for the climate and the environment” (EU regulation 1307/2013), namely i) arable crops diversification, ii) maintenance of permanent grassland and iii) ecological focus areas (EFA). These […]
Exposure to aeroallergens is associated with allergic rhinitis and asthma that significantly reduce quality of life and have a notable economic impact on society in the Croatia-Serbia cross-border region (Figure 1). Numerous sources of allergenic airborne pollen are present in the region resulting in exposure to strong allergens (e.g. birch trees, grass, common ragweed) on […]
The kickoff workshop of the ECMI SIG “Mathematics for Big Data” took place at University of Novi Sad on May 31 – June 1, 2017. The workshop was attended by more than 80 participants, coming from all Europe and also Australia. We had 14 speakers presenting talks ranging from optimization, learning and statistics for Big […]
Dates: May 31- June 1, 2017 Place: University of Novi Sad, Serbia The availability of huge amounts of data is often considered as the fourth industrial revolution we are living right now. The increase in data accumulation allows us to tackle a wide range of social, economic, industrial and scientific challenges. But extracting meaningful knowledge […]
