Motivation and Goals of this Position

Today, Process Analytical Technology (PAT), including but not limited to online spectroscopy and real-time state estimation by mechanistic models, is a main driver to transform the resource intense pharma industry, which is mostly based on single batches and their release to a continuous and resource efficient manufacturing scenario.

In order to derive sensor calibration or mathematical process models, considerable resources and innovative scientific approaches need to be allocated to i) systematic data collection and laboratory analyses, ii) model development and iii) model maintenance which currently hampers the efficient implementation of PAT approaches in the (bio-) pharmaceutical industry. Those elements are part of transfer learning strategies of models for PAT.

In close cooperation with two biopharmaceutical producers and experts in transfer learning, the PhD thesis aims to:

  • Apply transfer learning techniques for model development, transfer and maintenance for a relevant biotechnological process.
  • Obtain necessary calibration and validation data in lab scale experiments.
  • Develop workflows which can be potentially used in an industrial setting.

Requirements

  • Master of Science in Bioprocess Technology, Biotechnology, Biostatistics, Electrical Engineering, Chemical Engineering. Process Engineering or similar.
  • Some experience in standard lab work and basics in aseptic work with biomaterial
  • Interest in physical and data driven process modelling and in-situ spectroscopy
  • Curiosity to test developments within the lab and transfer them to industrial environments
  • Willingness to intensify numerical programming skills using tools such as Matlab® and Python®
  • A superior command of English
  • You should be accustomed to networked critical analytical thinking, scientifically interested and able to work in a team.

Opportunities

  • Work in a multidisciplinary Team
  • Connect with industry and learn their language and thinking
  • Intensify skills in highly requested field of data science
  • Possibility to guide your own PhD project with the possibility to collaborate with other team members
  • This PhD position runs within the Consortia Interpretable and Interactive Transfer Learning in Process Analytical Technology funded by the FFG.
  • This PhD position starts on May 1st 2021 and is scheduled for 3 years. The monthly minimum wage is currently € 2.148, 00 (14x per year), before tax, at a 30h/week employment.
  • The TU Wien aims to increase the proportion of women and encourages qualified women to apply.

We are looking forward to your application!

    2021-02-11T14:19:11+01:00