The chair of Prof. Bajcinca focuses on research of modern methods and advanced applications of control and system theory, involving three main pillars: cyber-physical systems, complex dynamical systems and machine learning. Through networking with a large number of national and international research, academic and industrial partners, funding projects with exotic and highly interesting tasks regarding model-based and data-driven control have been acquired on a regular basis. The research work is supported with an excellent laboratory equipment and high-performance computation in the areas of autonomous systems, robotics and energy systems, which is continuously being further developed.

https://www.mv.uni-kl.de/mec/home.
 

Cancer is a complex disease with highly nonlinear and diverse dynamics. Consequently, treatment of cancer is even more complicated and requires the use of sophisticated analytical and statistical algorithms. Developing effective treatment protocols thus requires the following steps: (i) Mathematical modelling: establishing a sufficiently precise yet generic mathematical description of the problem. (ii) Mathematical analysis: establishing well-posedness results and stability properties of the model. (iii) Numerical simulation (validation): phenomenological and numerical comparison of the model performance with the results obtained from experimental data or available literature. As part of the programme, you will be working on one or more of the above steps focusing on some of the core problems in the area of cancer genesis and migration.
 

Your key responsibility includes:

• Multiscale modelling of a particular type of cancer (e.g. Glioblastoma)
• To formulate key questions and establish mathematical results that supports the theoretical validity of the model
• Well-posedness and stability analysis of the model
• Verification and validation of the model
• Formulation of a control problem and implementing a treatment algorithm
• Verification and validation of the treatment algorithm
• Incorporation of AI methods is encouraged
   

• Above average university degree in applied mathematics, biomathematics, control engineering, bioinformatics or related disciplines
• Biological knowledge of cancer is expected
• Knowledge in mathematical modelling and analysis is expected
• Knowledge of at least one programming language: Matlab, Python, C++ is expected
• Highly motivated, eager to work within a team or independently
• Proficiency in English is essential. Knowing German is an advantage
   

• Payment according to TV-L E13 with an initial one-year time limit
• The possibility to do a PhD and to teach is given in case of scientific aptitude
• TUK strongly encourages qualified female academics to apply
• Severely disabled persons will be given preference in the case of appropriate suitability (please enclose proof)
• Electronic application is preferred. Please attach only one coherent PDF.

You can expect an interesting, diversified and responsible task within a young, highly motivated and interdisciplinary team of a growing chair with great personal creativity freedom.

Prof. Dr.-Ing. Naim Bajcinca
Phone: +49 (0)631/205-3230
Mobile: +49 (0)172/614-8209
Fax:  +49 (0)631/205-4201
Email: mec-apps(at)mv.uni-kl.de

Cancer modeling
Partial differential equations
Mathematical analysis
Numerical simulations
Machine learning
 

Cover Letter
CV
University Certificates
References
List of Publications

31. Oktober 2023

Immediate