PHA is a biodegradable bioplastic that can be produced from waste stream feedstocks by mixed microbial cultures according to the 2-step process: in the first step volatile fatty acids (VFAs) are produced from waste stream feedstocks. These VFAs are then utilized as building blocks for bacterial PHA production. A key challenge in this process is optimizing the yield, composition, and purity of the VFAs produced from residual streams. This project at MNEXT aims to address this bottleneck by developing a modeling system capable of effectively predicting and improving full-scale VFA production. Producing VFAs in a controlled manner will greatly benefit the predictability and stable production of PHA with specific properties. As an intern, you will contribute to this project with the following activities:
- Collect data for accurate predictive modeling and in-silico model validation
- Assess the data critically using basic data analysis techniques and statistics
- Contribute to predictive modeling and process optimization using state of the art machine learning algorithms
- The division between experimental and computational work is negotiable based on experience and interest
Desirable skills/qualities
- Biotechnology, chemical engineering, environmental engineering, biochemistry, chemistry or biology student (or related field) with basic understanding of bioprocesses and fermentation
- Previous experience in the lab with lab-scale bioreactors and basic analytical techniques. Able to work independently after initial training period
- Experience with data analysis and basic understanding of statistics
- Familiarity with basic machine learning techniques and programming in python is a plus
- Motivated, enthusiastic and a strong team player
What we offer
- Compensation of 350 euros/month
- An ability to grow and develop yourself in a supportive environment to improve both your experimental and computational skills
- Possibility of dissemination the results by participating in manuscript drafting and presentations at conferences