COSTNET Early Career Training Event
On behalf of the European Cooperation for Statistics of Network Data Science (COSTNET) a three-day Early Career Training Event will be held at the Department of Statistics at LMU Munich. It will take place from February 11, 2019 until February 13, 2019. The workshop will start on Monday at 9 a.m. and end on Wednesday at 5 p.m.
The aims of the event are twofold. First, we want to give PhD students and Postdocs the opportunity to participate in two short courses by experienced and renowned researchers. Second, the participants also will have the opportunity to present and discuss their own research.
Besides scientific exchange, the workshop is intended to enhance networking (in the non-statistical sense) among young scientists working in similar fields and should improve the transfer of cutting edge research within COSTNET. The social program will involve a guided city tour through Munich and a workshop dinner.
Please register below until December 20, 2018 (11 am).
Due to financial restrictions, only a limited amount of participants can be supported by our COST Action. You will be informed in a timely manner after the registration deadline whether you will be eligible for refund.
The social program is on a self-pay basis.
If you have any questions or should you need to cancel your participation please contact Verena.Bauer@stat.uni-muenchen.de.
The Early Career Training Event will take place in Professor-Huber-Platz 2 room: W 401 Ludwdig-Maximilians-Universität (LMU) Munich.
|Monday, February 11||Short course by Tiago de Paula Peixoto (University of Bath)||Abstract||City Tour (self paid, about 15€)|
|Tuesday, February 12||The participants present their own research in short talks (max. 20-25 minutes incl. discussion). The research talks can cover the general PhD topic, work-in-progress or completed work.||We expect all applicants to submit an abstract for the research talks.||Workshop Dinner (self paid)|
|Wednesday, February 13||Short course by Tom A.B. Snijders (University of Groningen, University of Oxford) on Statistical Methods for longitudinal Network Analysis||Abstract|