12th Nov 2024, 13:30 - 6 pm, Building 09.21, Blücherstr. 17, Karlsruhe
Details11th Nov 2024, 4 - 6:30 pm; Building 20.30, Room 3.069
More Information6th November 2023, 10 am, Deutscher Wetterdienst, Offenbach
AgendaOctober 5th, 2023; 10:00 am; Triangle OPEN SPACE
More InformationSeptember 19, 2022; 09:00 am; Triangle OPEN SPACE
More InformationJuly 7th 2022; 8:30 am; Triangle OPEN SPACE
More InformationJanuary 22, 2021; 09:00 am; Online
More InformationMarch 29 2021, 1:00 pm; Online
More InformationMarch 11, 2021, 10:00 am; Online
More InformationWorkshops
The KIT Centre MathSEE regularly organises workshops on interdisciplinary topics and aspects concerned with the elaboration and application of mathematical methods to enhance research and understanding in SEE disciplines. At the same time, these workshops on application-oriented topics provide mathematicians with interesting and challenging new topics to advance research in mathematical methods. Our latest workshop in October 2023 will be held on Deep Learning - Recent Advances in Kernel Methods for Neural Networks.
Regular workshops to strengthen links between KIT institutions and facilitate networking among members are also organised by MathSEE. MathSEE has successfully obtained internal funding for a joint workshop with the KIT Climate and Environment Center on "Combining theory-based knowledge with learning from data in earth system and environmental sciences". A workshop on this topic will take place in March 2021.
MB2 is organising the next thematic meeting under the title "Anything related to Covid-19". The aim is to provide a quick and easily organised overview of our contributions to the scientific assessment of different aspects of the pandemic, e.g. aerosol simulation, pandemic differential equation modelling, teaching basic mathematical biology to students, science communication, etc.
In addition to the workshops organised by the structured programmes, MathSEE co-organised a workshop on Data Science for Materials Science. The workshop aimed to address the growing amounts of data in different branches of materials science