JOB DESCRIPTION

Collaboration
This PhD project is part of a collaboration between Delft University of Technology (TU Delft) and University of Colorado Boulder (CU Boulder). The partnership includes three TU Delft research groups at the Department of Biotechnology (dr. Klijn, dr. Vieira-Lara, and dr. Jourdin) and one CU Boulder research group embedded in the Department of Mechnical Engineering (prof. Rieker). The overarching aim of this collaboration is to integrate and apply a novel analytical technique to perform real-time spectroscopic monitoring of the bioreactor off-gas for two different types of bioprocesses, namely microbial fermentation in suspension and biofilm-based microbial electrosynthesis. With this technology we set out to advance bioprocess understanding and accelerate bioprocess optimization, scale-up, and automated control.
Team
The content of the partnership leads to highly interdisciplinary and collaborative research. The complete team to be implemented at TU Delft will consist of 2 post-doc positions (1. quantitative monitoring of MES and 2. quantitative monitoring of microbial suspension cultures), 2 PhD candidates (1. volatile fingerprints of microbial suspension cultures and 2. data analytics/machine learning and control), and 2 technicians (1. support MES cultures and 2. support suspension cultures, both are involved with tech transfer and integration). In addition, there are three research positions part of prof. Rieker’s team at CUB with which we will closely collaborate throughout the project. We are looking for people who can showcase their ability to work well in a team, are effective in sharing theoretical knowledge and technical know-how, and excell as creative problem solvers.
PhD project
The PhD position in this vacancy will focus on the development, implementation, and validation of quantiative and fingerprint-based data-driven models using (near) real-time novel spectroscopic off-gas data. You will have the opportunity to work with a wide range of analytical data, including, but not limited to: liquid, off-gas, and mass spectral data, transcriptomics, and gas and liquid chromatography data. The data will be applied to perform comparability studies, build data-driven models, and mining for novel volatile biomarkers. Moreover, the data generated with the real-time data-driven monitoring models will be used as input for hardware-based feedback control of the bioprocess, which you will develop as well.
This project is computational, with a strong emphasis on statistics, data analytics, and machine learning, and requires close collaboration with the experimental projects in the team described above. Therefore, it is considered a plus if you have an solid understanding or experience with processes in biotechnology, as it will support communication with the other team members. Your experience with or motivation for the bioprocessing field needs to be clearly indidacted in the motivation letter.
The PhD position is embedded with the Data-Driven Bioprocess Development group led by dr. Marieke Klijn. In this group, we work on a wide variety of biotechnological processes (microbial, mammalian, ATMP) and the implementation of process analytical technology (such as FT-IR and Raman spectroscopy) for process development and process automated control purposes. The research group currently hosts 6 PhD project and is closely connected to other research groups within the Bioprocess Engineering section (~25 PhDs/postdocs) at the Department of Biotechnology. This organizational structure provides ample opportunities for research cross-polination and a social framework.

JOB REQUIREMENTS

We are looking for an outstanding and creative researcher with a strong affinity to work at the boundary of multiple disciplines, namely bioprocessing, data analytics, and hardware control. This includes the internal motivation to expand your horizon, ablilty to learn quickly, and strong communication skills to easily connect with all team members.

Required educational backgroud

• A MSc degree in biotechnology, bioprocess or chemical engineering, bioinformations, bioengineering, systems biology, or a closely related discipline

Recent experience or clearly supported motivation

• MSc thesis or MSc internship using data analytics, mechanistic modelling, data-driven modelling, and/or hybrid modelling in the field of biotechnology/bioinformations/bioprocess engineering/chemical engineering

Required technical skills

• Programming (Matlab/Python/R)
• Statistical or machine learning methods
• Ability to process and analyse multiplexed and large data sets

Skills that are considered a plus

• Practical experience with bioreactors (microbial/mammalian)
• Understanding of bioprocesses (design, transport phenomena)

Sought-after transferable skills

• Pro-active and problem solving mindset
• Excellent written and spoken English skills
• Ability to break-down and communicate complex research data
• Thrives in a fast-paced, multidisciplinary, and highly collaborative environment

Please refer the Job description for details