Funding
Our research at Bielefeld University is funded through the following grants. For funding at Helmholtz Munich, please see the chair holder‘s ORCID page.
Coping with Uncertainty in Complex Economies (CUDE)
German Research Foundation
Consortium coordinator: Frank Riedel
Further principal investigators: Yves Breitmoser, Herbert Dawid, Giorgio Ferrari, Manuel Förster, Christiane Fuchs, Dominik Karos, Max Nendel, Maren Schmeck, Anna Zaharieva
Funding Period: 2023-2027
The Research Training Group CUDE focuses on the analysis of dynamic economic models under uncertainty, e.g. in the context of labour markets or epidemics. It studies foundations of individual and social behavior under uncertainty and dynamics, focuses on implications of uncertainty in markets and strategic interactions, and addresses questions of policy and welfare.
More information: Project webpage
Novel statistical solutions for optimized experimental design hand in hand with domain expertise (OPTI-TRIALS)
Federal Ministry of Education and Research and European Union
Consortium coordinator: Christiane Fuchs
Project members from Bielefeld University: Julian Wäsche, Houda Yaqine
Project partners:
- Research Unit Apoptosis in Hematopoietic Stem Cells at Helmholtz Munich (Irmela Jeremias)
- Institute for Metallic Biomaterials at Helmholtz-Zentrum Hereon (Berit Zeller-Plumhoff)
- Core Facility Statistical Consulting at Helmholtz Munich (Elmar Spiegel)
Funding period: 2022 - 2025
Medical research uses laboratory animals where in vitro experiments are not an alternative. The use not only requires financial investment, but also raises ethical questions. The project aims to find ways to reduce the number of laboratory animals used in medical tests while gaining more knowledge. To this end, methods are to be developed that can deal with fewer measurement points and still provide meaningful data. Mathematical models, estimation procedures and hypothesis tests, as well as software and training courses, are being developed using the example of two selected applications, namely transplantation models in cancer research and implant models in materials research. The aim of this project is to establish such routines interdisciplinarily at the operational level of young scientists and to anchor them in the individual disciplines.
More information: Project website
AI-based decision support for antibiotic therapy (KINBIOTICS)
Federal Ministry of Health
Consortium coordinator: Philipp Cimiano
Further PIs from Bielefeld University: Christoph Dockweiler, Christiane Fuchs , Wolfgang Greiner, Claudia Hornberg, Jörn Kalinowski, Alexander Sczyrba
Funding period: 2020-2023
With increasing antibiotic resistance, tools to help prescribe specific antibiotics with minimal side effects are gaining importance. The KINBIOTICS project focuses on particularly difficult bacterial infections such as sepsis. The consortium brings together experts from the clinics as well as biotechnology and health, computer and data sciences. It aims to provide AI-based decision support to prescribe antibiotics within hours of detecting an infection.
Further information: Project website
Uncertainty Quantification - From Data to Reliable Knowledge (UQ)
Helmholtz Association, Pilot Project in Information & Data Science
Consortium coordinators: Martin Frank, Christiane Fuchs
Funding period: 2019-2024
Uncertainty is ubiquitous in models and data. From stochastic modelling, where fluctuations play a central role in the dynamics of the process, to data collection, where measurement and sampling error permeate the data, it is central to understand the effects of uncertainty. The UQ project is centered on placing these elements in focus. The consortium spans 10 institutions with domain researchers, statistical and mathematical methods researchers, and research software engineers who care about quantification of uncertainty.
More information: Project webpage
Chronic pain in patients with and without inflammatory rheumatic disease in primary and secondary care: trans-sectoral inspection, review of a new referral strategy and analysis of context factors (CRESCENT)
Anschubfonds Medizinische Forschung, Bielefeld University
Principal investigators: Martin Rudwaleit (Klinikum Bielefeld), Christiane Fuchs, Sebastian Rehberg (Bethel), Wilfried Witte (Bethel)
Funding period: 2020-2023
Chronic pain in the musculoskeletal system is a frequent reason for a visit to the doctor, for sick leave, assessments and retirement. The causes are degenerative changes, inflammatory rheumatic diseases and chronic pain syndromes such as fibromyalgia. It is sometimes difficult to differentiate between these diseases in primary care, which is why many patients are referred to rheumatology, leading to long waiting times. The project pursues the goal of an improved, computer-supported preselection for urgent appointments in rheumatology with simultaneous detection of a possible fibromyalgia syndrome already in primary care.
It tests a new, simple referral path with standardised assessments in primary medicine, rheumatology and pain therapy for the analysis of context factors in fibromyalgia.
Economic Policy in Complex Environments (EPOC)
Innovative Training Network, Horizon 2020
Consortium coordinator: Herbert Dawid
Further PIs from Bielefeld University: Manuel Förster, Christiane Fuchs, Roland Langrock
Funding period: 2021-2025
Many of the main current economic and social challenges facing Europe are characterised by complex dynamic patterns. Examples include the search for appropriate policy measures to mitigate climate change or to manage the development, diffusion and economic impact of new technologies. The Innovative Training Network aims at promoting the state of the art and the applicability of computationally intensive methods for decision and policy analysis and their application in the fields of climate change and innovation. Next to Bielefeld University, the network includes universities from the Netherlands, Spain, Denmark, Italy and France.
Statistical postprocessing of ensemble forecasts for various weather quantities
Grant Holder: Annette Möller
Further members: Sándor Baran, Patricia Szokol, Jürgen Groß, Roman Schefzik, Sebastian Lerch, Stephan Hemri
Funding Period: 2018-2023
Accurate prediction of future weather events is increasingly important in many areas of society and economy. State-of-the art is the use of so-called numerical weather prediction (NWP) models are typically used for weather prediction and obtain an ensemble of forecasts based on multiple runs of the model. However, these ensemble forecasts typically lack proper calibration and thus require postprocessing by statistical models. These models are applied to the forecasts in conjunctions with observations in order to improve the quality of the forecasts. In addition, many statistical postprocessing approaches obtain a probabilistic forecast, often in terms of a full predictive probability distribution, which allows to assess and quantify forecast uncertainty.
In the Scientific Network " Statistical Postprocessing of ensemble forecasts for various weather variables" funded by the German Research Council (DFG) a group of international researchers is working jointly on developing and implementing classical statistical methods as well es more modern methods from the area of machine learning for probabilistic forecasting of different weather variables. An additional focus of the research is to incorporate multivariate dependencies into the postprocessing models in order to obtain physically coherent forecasts.
Big Data Beauty – The aesthetic potential of large numbers and algorithms
Fellowship for innovations in digital university teaching, Stifterverband für die Deutsche Wissenschaft
Grant holders: Turid Frahnow, Johannes Voit
Funding period: 2018-2019
The digital revolution has brought us not only a multitude of technical innovations, but also a flood of data that exceeds human capacity. The algorithms used to analyze this data are ubiquitous, whether in navigation devices or in weather forecasting. But algorithms are by no means just abstract procedures, they often have a very practical meaning for everyday actions. And beyond their practical use, they can even hold artistic potential. As part of this project, we held an interdisciplinary seminar in which the aesthetic potential of algorithms and large numbers was explored. Outcomes were presented at the jubilee festival in September 2019. We have also contributed an exhibit piece to the university's showroom a sounding of a sorting algorithm using sounds from the university building.
The project was also financially supported by the University of Bielefeld as a jubilee project.
Some student projects from the seminar are presented here (in German).
Our main building: success factor for interdisciplinary research or architectural sin?
Scientific jubilee project funded by the Faculty of Business Administration and Economics at Bielefeld University
Grant holders: Hannah Busen, Christiane Fuchs
Funding period: 2018-2019
It seems evident that spatial proximity between researchers may lead to more frequent or more intense collaboration than between scientists who work at large distance from each other. We hypothesize that the spatial organization within a research campus or even within a building influences interdisciplinary work. In a collaboration network study, we investigate which distance matters, how much researchers are influenced by people working around them and how scientific publishing changes depending on the heterogeneity among authors.