Bioinformatician with focus on research support in image analysis (Stockholm, Sweden)
Organization: KTH Royal Institute of Technology
KTH Royal Institute of Technology, School of Computer Science and Communication
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.
The position will be formally placed with the department for Computational Science and Technology (CST) at KTH, but work will be carried out at the Science for Life Laboratory. The CST department conducts research to understand and model physical and biological systems using computational techniques, which require efficient, high performance algorithms and implementations together with advanced visual analysis capabilities. For more information go to https://www.kth.se/en/csc/forskning/cst. The Science for Life Laboratory (SciLifeLab) is a collaboration between four universities in Stockholm and Uppsala: Karolinska Institutet, KTH, Stockholm University and Uppsala University. It combines advanced technology with broad knowledge in translational medicine and molecular life sciences. Since 2013, SciLifeLab has a mission from the Swedish government to run infrastructure to support researchers nationally and to be an internationally leading center for large-scale analyses in molecular life sciences targeting research in health and environment. For more information, visit https://www.scilifelab.se/.
We are looking for a PhD with a keen interest in implementation and adaption of image analysis algorithms for quantitative analysis of microscopy data to join the SciLifeLab BIIF (https://www.scilifelab.se/facilities/bioimage-informatics/). You will work in short and long-term projects, providing research support and education in image analysis. The BIIF has nodes in both Uppsala and Stockholm, but the assignment includes services to researchers at other universities in Sweden as well. The job includes involvement and organization of courses and workshops in digital image processing and analysis with life science applications. Supervision of master thesis students is part of the assignment. 20% time will be spent supporting projects at the LCI facility in Huddinge.
A PhD in computer vision/image processing (or equivalent), or a PhD in bio-medicine/biology (or equivalent), combined with documented experience of computer programming and development of digital image analysis solutions is required. In addition, the following qualifications are considered highly desireable:
- Experience with analysis software such as Imaris, ImageJ/Fiji, CellProfiler, NIS and Amira
- Experience with algorithm development and development of analysis pipelines aiming to quantify and classify biological parameters extracted from microscopy data of e.g. cells and microorganisms in cultures and tissue samples
- Experience in medical image analysis
- Experience with teaching and student supervision
- Proficiency with a scientific programming language (Python, R, or Matlab)
- Excellent English communication skills, both spoken and written, as well as excellent interpersonal skills Other meriting experiences and skills
- Postdoctoral experience in image analysis, within or outside academia, especially focused on methods development
- Familiarity with deep learning frameworks like Keras, TensforFlow, or PyTorch
Trade union representatives
You will find contact information to trade union representatives at KTH’s webbpage.
Log into KTH’s recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad. Your complete application must be received at KTH no later than the last day of application.
Applications shall include the following documents:
- Statement of interest
- A description of experience in bioimage analysis and deep learning
- Curriculum vitae
- Reference contact information
- Maximum 2 representative publications (or other example of scientific writing)
Please observe that all material needs to be in English.