The University of Bonn is an internationally operating research university with a broad spectrum of subjects. With 200 years of history, around 38,000 students, more than 6,000 employees and an excellent reputation: The University of Bonn is one of the most important universities in Germany. In the recent excellence initiative, the University of Bonn was the most successful university in Germany with six Clusters of Excellence.
In this excellent scientific environment, the Interdisciplinary Research Unit Mathematics and Life Sciences (https://www.mathematics-and-life-sciences.uni-bonn.de/en) develops and applies novel mathematical approaches and software tools for data analysis and modelling. Application areas include cancer and immunology, with an emphasis on high-throughput (single-cell) data. We are intensively collaborating with world leading experts in mathematics and immunology in the Hausdorff Center for Mathematics, ImmunoSensation2, and the German Center for Neurodegenerative Diseases. Currently, we are searching for individuals who strengthen our team.
Elevated blood glucose concentrations (hyperglycaemia) in patients undergoing clinical treatment are a common, serious and costly health problem with serious medical consequences. Nearly 40% of patients experience hyperglycaemia during hospitalization, predominantly in patients with known diabetes. Research studies have shown that improved blood glucose control in patients undergoing clinical treatment can reduce the risk of complications (e.g., new infections during treatment) and mortality (Umpierrez et al., J. Clin. Endocrinol. Metab., 2002). For this reason, the cooperative project GlukoSys (4 companies and 2 research institutes) aims at developing an integrated system for monitoring and controlling blood glucose levels in intensive care patients. The system will enable continuous and individual blood glucose monitoring of patients for the first time and is adapted to hospital standards.
The aim of the subproject led by our research group is to develop a software which (1) identifies critical situations at early stages and (2) predicts optimal insulin and glucose infusion rates. The software will exploit continuous measurements of the blood sugar levels provided by a novel monitoring device. In combination with the monitoring systems, the automated closed-loop control should facilitate the stabilization of patients, thereby reducing the risk of complications during intensive care.
- Development of model and algorithms for closed-loop control of glucose levels for intensive care patients
- Application of methods and interpretation of the results
- Implementation of methods in a clinical setting
- Collaboration with national and international partners
- PhD degree in biomedicine, engineering, mathematics, physics or equivalent
- Background in mathematical modeling (e.g., ODE models), control, optimization or machine learning
- Programming experience (preferably Python and/or C++)
- Experience with clinical practice is an asset
- Being comfortable in interacting with colleagues in an interdisciplinary setting
- Proficiency in written and spoken English
- Passion for science and scientific work
- Working in an innovative, well-equipped and scientifically stimulating environment
- Further training opportunities
- Initial short-term employment contract for 2 years with a standard public service salary (TV-L EG 13, 100%)
The University of Bonn is committed to diversity and equal opportunity. It is certified as a family friendly university. It aims to increase the proportion of women in areas where women are under-represented and to promote their careers in particular. It therefore urges women with relevant qualifications to apply. Applications will be handled in accordance with the Landesgleichstellungsgesetz (State Equality Act). Applications from suitable individuals with a certified serious disability and those of equal status are particularly welcome.
The deadline for the application round is October 31, 2019. Application documents (cover letter, CV, certificates, and two reference letters or contact details of former advisors) should be submitted as soon as possible as a single PDF file via email.
Contact: Prof. Dr. Jan Hasenauer, firstname.lastname@example.org