Survival Analysis for Junior Researchers (SAfJR) Ulm, 13th - 15th September, 2023
SAfJR is an event that is aimed at career-young statisticians with an interest in survival analysis and related research areas. The conference offers an excellent opportunity for young researchers to present and discuss their work with participants at a similar phase in their careers. Keynote speakers, a tutorial, an awarded poster session and a conference dinner are the aims of the Organizing Committee.
Early stage researchers and statisticians, such as
"Newbie" researchers and statisticians
Anyone who does not consider themselves as a "young researcher or statistician" is also welcome to attend and present their work.
Tutorial of Torsten Hothorn, University of Zurich (CH)
Keynote talk of Torben Martinussen, University of Copenhagen (DK)
Keynote talk of Caroline Foch, Merck Healthcare KGaA (GER)
- Start: 13. March 2023
- Deadline: 30. April 2023
- Decision: 01. June 2023
More information: Here
- Start: 19. June 2023
- Deadline Early-Bird: 21. July 2023
- Close: 31. August 2023
Organizing Committee´s Responsibilities
Sandra Schmeller is a doctoral candidate in Biostatistics at Ulm University. Her current research interests involve multistate models, especially in the field of stem cell transplanted patients data.
Alexander Stemke is a doctoral candidate in Statistics at Ulm University and employee at Boehringer Ingelheim. His research interests involve Bayesian survival analysis, especially with regards to safety analysis and causal inference. Currently dealing with meta-analytic predictive priors to enhance clinical trials.
Judith Vilsmeier is a doctoral candidate in Biostatistics at Ulm University. Her current research interests involve nonstandard event histories and estimation of complex outcomes in non-Markov multistate models.
Jasmin Rühl is a doctoral candidate in Biostatistics at Augsburg University. Her research addresses inference for causal effect estimates in survival data based on different resampling methods
Dr. Jan Feifel is a statistician and data scientist at Merck KGaA. He focuses on machine/statistical learning and time-to-event methods for real-world data across therapeutic areas.
Also, he is contributing to the COMBACTE consortium developing non-standard sampling designs and is curious about causal inference.