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

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

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🗓️ Posted 2026-10-02 Sweden - Gothenburg Full-time Hybrid Science Technology
💰 Salary Not shared 📊 Market 20,000 GBP–30,000 GBP/mo

About the Company

Are you passionate about combining laboratory science, digital pathology, and artificial intelligence to advance disease research In this thesis project, you will contribute to innovative approaches for visualising and quantifying disease-related changes, supporting the development of smarter tools for studying respiratory and immune-mediated diseases. AstraZeneca is a global, science-led, patient-centered biopharmaceutical company focusing on discovering, developing, and commercializing prescription medicines for some of the world's most serious diseases. But we're more than a global leading pharmaceutical company. At AstraZeneca, we're dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit. About the Opportunity: As a Thesis Worker at AstraZeneca, you'll find an environment that's full of unique opportunities and exciting challenges. Here, you'll have the opportunity to pursue your areas of interest whilst equally developing a broad skillset and knowledge base to get the best out of your experience. You'll be working on meaningful projects to make an impact and deliver real value for our patients and our business. Thesis work description: Conventional histochemical stains, such as haematoxylin & eosin, are the workhorses of tissue pathology, yet the rich biological information they encode - about cell types, tissue compartments, and disease-related changes - remains largely underutilized compared to what multiplexed immunofluorescence (IF) can reveal. This thesis project investigates whether deep learning can bridge that gap by predicting virtual multiplexed IF directly from conventional stain images, unlocking marker-level information without additional staining steps. The project involves wet-lab work, conventional histochemical and IF staining, whole-slide tissue scanning, and image analysis using AI. You will work with mouse and human tissue from respiratory and immune-me

Responsibilities

  • ● Staining optimization - refine pretreatment conditions for combined IF-convential histochemical protocols on the same tissue section, preserving both conventional stain quality and fluorescence signal integrity.
  • ● Immunofluorescence multiplexing - design and optimize multiplex immunofluorescence panels targeting key cell types and tissue compartments in respiratory and immune-mediated indications.
  • ● Virtual IF prediction - apply a deep learning pipeline to translate conventional histochemical stain images into predicted fluorescence outputs across multiple tissue types to perform cell classification
  • ● Quantitative validation - benchmark predicted outputs against matched ground-truth data to assess accuracy and biological relevance.
  • ● Duration: Start Spring term 2027
  • ● Credits: 60
  • ● Enrolled in a Master's program within biomedical engineering, biotechnology, bioinformatics, computational biology, biomedicine, molecular biology, pathology, data science, computer science, or a related field.
  • ● Basic practical laboratory experience, preferably including histology, immunohistochemistry (IHC), immunofluorescence (IF), or assay optimization.
  • ● Ability to work carefully, reproducibly, and in accordance with laboratory protocols.
  • ● Interest in, and preferably some experience with digital pathology, image analysis, machine learning, or deep learning.
  • ● Ability to plan experiments, analyze quantitative data, and document methods and results in a structured and traceable manner.
  • ● Strong problem-solving ability, attention to detail, and a systematic approach to experimental optimization and validation.
  • ● Ability to work independently and collaborate closely with multidisciplinary colleagues.
  • ● Excellent communication skills in English, both verbally and in writing.
  • ● Experience with multiplex staining, whole-slide imaging, tissue segmentation, cell phenotyping, or digital pathology software.
  • ● Familiarity with basic statistical analysis.
  • ● Interest in respiratory or immune-mediated diseases.