Thesis Work, 60 Credits - Development of Multiplex Virtual Cell Classification from Conventional Histological Stains for the Assessment of Respiratory and Immune-Mediated Disease Indications

AstraZeneca · Gothenburg, Sweden

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Role summary

This 60-credit thesis project investigates deep learning approaches to predict multiplexed immunofluorescence markers from conventional histological stains, integrating wet-lab staining optimization, tissue imaging, and AI-driven analysis for respiratory and immune-mediated disease research at AstraZeneca Gothenburg.

What they're looking for

Enrolled in Master's program in biomedical engineering, biotechnology, bioinformatics, or related field
Basic practical laboratory experience with histology, immunohistochemistry, immunofluorescence, or assay optimization
Interest in and preferably some experience with digital pathology, image analysis, or machine learning
Strong problem-solving ability, attention to detail, and systematic experimental approach
Ability to work independently and collaborate with multidisciplinary teams
Excellent English communication skills, both written and verbal
Capable of planning experiments, analyzing quantitative data, and documenting results systematically

What you'll be doing

Optimize staining protocols combining immunofluorescence and conventional histochemistry on tissue sections
Design and develop multiplex immunofluorescence panels targeting relevant cell types and tissue compartments
Apply deep learning pipelines to predict fluorescence outputs from conventional histochemical stain images
Perform quantitative validation of predicted outputs against ground-truth data for accuracy assessment
Execute wet-lab work including tissue staining, whole-slide imaging, and spatial biology techniques

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