Analysis, Design and Optimization in Chemical and Biochemical Process Engineering: International Max Planck Research School
- Degree
- Ph.D.
- Address
- Analysis, Design and Optimization in Chemical and Biochemical Process Engineering: International Max Planck Research School, Max-Planck-Institut für Dynamik komplexer technischer Systeme, Dr. Barbara Witter, Sandtorstraße 1, Magdeburg, 39106, Germany
- Ads
-
- Subject
-
Biochemical Engineering
- Course Language(s)
- English
- Course Beginning
- 1-Oct
- Duration
- 6 years. Students are expected to finish their Ph.D. thesis within a period of 3 years.
- App Deadline
- There will probably be two consecutive rounds of application, the first for 2007/2008 - the second application round will be for 2010/2011.
- Submit Application To
Please use for your application our application form on our website under: www.pe-imprs.mpg.de
- Telephone
- +49.391.611.0209
- Fax
- +49.391.611.0203
- E-mail
imp...@mpi-magdeburg.mpg.de
- Website
- http://www.pe-imprs.mpg.de
The major goal of this joint initiative of the Max Planck Institute for Dynamics of Complex Technical Systems and the Otto von Guericke University Magdeburg is to provide excellent training and Ph.D. research on the analysis, the design and the optimisation in chemical and biochemical processes engineering.
The approach aims to foster a close interplay between theory and experiment and to promote the use of mathematical modelling approaches based on a thorough understanding of the fundamental principles of chemical and biochemical processes.
The focus of the IMPRS is on:
- the development and the application of mathematical modelling approaches and systems-theoretical methods to identify systems structures, to investigate process behaviour and to design technical processes for specific tasks,
- the experimental characterisation of challenging technical processes to identify key components of structure and function, to analyse systems under a variety of different process conditions, to characterise influence on modifications and perturbations on process performance, to enhance process yields and to validate mathematical models.
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