Thesis: AI-Driven Protein Expression Lab (Gothenburg)

AstraZeneca; Göteborgs kommun; On-site; SEK 156,000 - 190,000; Full time

Job summary

AstraZeneca Gothenburg offers a hands-on master’s thesis position focused on producing and quality-checking recombinant proteins in E. coli, exploring expression conditions across 97 constructs, and contributing to next-generation protein expression models.

You will work in the Ligand-AI project, analyzing experimental data against RP3Net predictions, with on-site placement, 30 credits, Spring 2027, and a practical path to impact patient-focused drug discovery.

Qualifications

• Enrolled in a Master’s programme within biochemistry, biotechnology, molecular biology, protein science, or a related field.
• Previous lab-based experience with recombinant protein production using E. coli.
• Expression of recombinant protein in E. coli and purification from E. coli cells, ideally using Ni-NTA chromatography.
• Experience with experimental lab procedures, documenting results, and troubleshooting.
• Interest in protein science, data analysis, FAIR data capture, and AI or computational models in biology.
• Strong analytical and problem-solving skills.
• Excellent English communication, including scientific writing and presentation.

Responsibilities

• Work experimentally with recombinant protein production using E. coli as the expression system.
• Express, purify, and quality-check proteins for screening of new ligands within Ligand-AI.
• Investigate how codon optimization and expression conditions affect protein production across 97 constructs.

Skills

Recombinant protein production

E. coli expression

Protein purification

Data analysis

Scientific writing

Education

Master's program in biochemistry or related

Tools

Ni-NTA chromatography

Job description

AstraZeneca Gothenburg offers a hands-on master’s thesis position focused on producing and quality-checking recombinant proteins in E. coli, exploring expression conditions across 97 constructs, and contributing to next-generation protein expression models.

You will work in the Ligand-AI project, analyzing experimental data against RP3Net predictions, with on-site placement, 30 credits, Spring 2027, and a practical path to impact patient-focused drug discovery.