PhD Scholarship: Microalgae for Therapeutic Protein Production

Nuclear Inst; United Kingdom; On-site; GBP 18,000 - 26,000; Full time

Job summary

The University of Manchester is offering a PhD project focused on developing microalgae as expression platforms for therapeutic proteins. The project explores growth and protein production in engineered Chlamydomonas strains, with a hybrid kinetic model to simulate cultivation and guide optimal conditions.

You will work on batch and fed-batch experiments, refine a Physics-informed Machine Learning model, and translate findings into scalable production scenarios for larger reactor systems,

Qualifications

• 2:1 honours degree in a discipline directly relevant to the PhD Chemical Engineering, Biotechnology, Chemistry, Physical Chemistry (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Chemical Engineering, Biotechnology, Chemistry, Physical Chemistry (or international equivalent).
• Previous experimental experience and/or computational experience is desirable.

Responsibilities

• Characterise growth and protein production of C. reinhardtii UVM11 expressing human erythropoietin (EPO) and mCherry.
• Develop a physics-informed kinetic model to simulate cultivation with nutrient consumption, biomass, and product formation.
• Optimise cultivation strategies and scale production from 12 to 60 photobioreactors.
• Collaborate with group researchers and funders to deliver robust scientific results.

Skills

Experimental experience

Computational experience

Education

Bachelor/Master level

Job description

The University of Manchester is offering a PhD project focused on developing microalgae as expression platforms for therapeutic proteins. The project explores growth and protein production in engineered Chlamydomonas strains, with a hybrid kinetic model to simulate cultivation and guide optimal conditions.

You will work on batch and fed-batch experiments, refine a Physics-informed Machine Learning model, and translate findings into scalable production scenarios for larger reactor systems,