PhD: AI-Driven Multimodal Biomaterials & Regenerative Imaging

Arbeidsplassen; Oslo; On-site; NOK 551,000 - 605,000; Full time

Job summary

University of Oslo is offering a three-year Doctoral Research Fellow position in AI-driven quantitative analysis and predictive modelling of biomaterials, tissues, and regenerative constructs. The project combines advanced 2D/3D bioimaging with machine learning, computational analysis and computer vision to link structure, mechanics and biology for tissue reconstruction.

The candidate will work with multimodal datasets, receive interdisciplinary training, and contribute to three planned

Qualifications

• MSc and BSc in Biomaterials, Biomedical Engineering, Biotechnology, Bioinformatics, Biology, Computer Science, Medical Technology, or related field.
• Relevant experience with biomedical imaging, quantitative data analysis, image processing, machine learning, or related methods.
• Demonstrated interest in interdisciplinary research combining biological and computational approaches.
• Fluent oral and written communication skills in English.

Responsibilities

• Perform quantitative 2D/3D analysis of biological tissues and biomaterials using micro/nanoCT, confocal microscopy and SEM.
• Characterize fibrous scaffold architecture, porosity and structural stability.
• Develop computer vision and machine learning methods for multimodal bioimaging data.
• Build AI models predicting cell and exosome distribution in regenerative biomaterials.

Skills

Fluent English

Independent work

Analytical skills

Scientific curiosity

Image processing

Machine learning

Biomaterials

Biological imaging

Education

MSc in Biomaterials

BSc in Biomaterials

Tools

Python

Job description

University of Oslo is offering a three-year Doctoral Research Fellow position in AI-driven quantitative analysis and predictive modelling of biomaterials, tissues, and regenerative constructs. The project combines advanced 2D/3D bioimaging with machine learning, computational analysis and computer vision to link structure, mechanics and biology for tissue reconstruction.

The candidate will work with multimodal datasets, receive interdisciplinary training, and contribute to three planned