Associate Researcher

The University of Gothenburg tackles society's challenges with diverse knowledge students and 6 000 employees make the university a large and inspiring place to work and study. Strong research and attractive study programmes attract scientists and students from around the world. With new knowledge and new perspectives, the University contributes to a better future.

The University of Gothenburg promotes equal opportunities, equality and diversity.

We are seeking a highly motivated Associate Researcher (Data Scientist) with a strong background in computational image analysis, particularly in multimodal imaging. Experience in machine learning is highly valued. You will support user-driven research projects and develop integrated data workflows spanning light microscopy (confocal, super-resolution, lightsheet), volume electron microscopy, and chemical imaging (nanoSIMS, MALDI-IMS).

You will join the Integrated Microscopy Technologies (IMT), part of the Cellular & Molecular Imaging Platform at SciLifeLab, which provides national access to advanced imaging, training, and technology development. The Gothenburg node (IMT-GU) brings together the Centre for Cellular Imaging at the Sahlgrenska Academy and NanoSIMS Sweden at the Faculty of Science and Technology, offering complementary expertise in microscopy, chemical imaging, and image analysis.

The position is based at IMT-GU, hosted by Core Facilities, University of Gothenburg. As part of the SciLifeLab Integrated Data Services (IDS) initiative, you will help build computational support for multimodal imaging and connect imaging technologies with data-driven life science.

Job Duties

• Implement and adapt image analysis pipelines for microscopy, chemical imaging, and correlative multimodal workflows. Focus on robust, reproducible solutions and apply machine learning tools where relevant.

• Enable user access to advanced analytical methods by developing user-friendly tools and ensuring workflow reproducibility.

• Support the use and deployment of image analysis pipelines on local and national high-performance computing resources in collaboration with NBIS, the SciLifeLab Data Centre, and DDLS fellows.

• Contribute to data stewardship by supporting metadata management, use of open data formats, FAIR principles, and data management plans, in coordination with local and national efforts.

• Provide user support and training through consultations, courses, and workshops.

• Contribute to national method development within SciLifeLab, focusing on cellular and multimodal imaging.

The position does not include own research; all development work is linked to user needs and scientific questions.

Qualifications

Qualification requirements:

• A university degree in computer science, physics, engineering, bioinformatics, or a related field (PhD degree is meritorious but not mandatory).
• Demonstrated expertise in image analysis, data science, or related computational research, with a strong track record in developing, applying, and supporting analytical methods.
• Experience with machine (deep) learning frameworks (e.g. PyTorch, TensorFlow).
• Excellent written and verbal English communication skills, required for daily interaction with an international user base.

Preferred qualifications:

• Proven experience applying machine (deep) learning to imaging data.
• Strong programming skills, preferably in Python, and use of version control (Git/GitHub).
• Knowledge of open-source image analysis software (e.g. Fiji, Napari, scikit-image).
• Familiarity with high-performance computing or cloud environments.
• Experience with multimodal or correlative imaging data (light/electron microscopy & chemical imaging).
• Experience in teaching computational methods.
• Knowledge of data conversion and handling standard formats (OME-TIFF, OME-Zarr).
• Knowledge of workflow management systems for analysis pipelines.
• Familiarity with data stewardship and FAIR principles.

Personal abilities

We are looking for a service-minded team player with a strong interest in enabling cutting-edge research through advanced image analysis. The candidate should be eager to expand their knowledge in new computational and imaging methods and thrive in a highly interdisciplinary environment. In a core facility, it is essential to manage multiple user projects and organize work within short timelines. The ideal candidate is enthusiastic and solution-oriented, combining technical expertise with strong communication skills and a genuine commitment to supporting other researchers.

Employment

It is a permanent employment and a full time position. A probationary period may be applied. The employment is placed at Core Facilities, Sahlgrenska Akademin in Gothenburg, Sweden.

Selection process

For more information regarding the selection process go to:

Contact information for the post

If you have any questions about the position, please contact Rafael Camacho at

Applications should not be sent to the contact person.

Unions

Union representatives at the University of Gothenburg can be found here:

Application

To apply for a position at the University of Gothenburg, you have to create an account in our recruitment system. Submit your application via the University of Gothenburg's recruitment portal by clicking the "Apply" button. It is your responsibility to ensure that the application is complete as per the vacancy notice, and that the University receives it by the final application deadline.

Applications must be received by:

The University works actively to achieve a working environment with equal conditions, and values the qualities that diversity brings to its operations.

Salaries are set individually at the University.

In accordance with the National Archives of Sweden's regulations, the University must archive application documents for two years after the appointment is filled. If you request that your documents are returned, they will be returned to you once the two years have passed. Otherwise, they will be destroyed.

In connection to this recruitment, we have already decided which recruitment channels we should use. We therefore decline further contact with vendors, recruitment and staffing companies.