Research Assistant in Computational Pathology

The Department of Medical Epidemiology and Biostatistics (MEB) is among the largest departments of epidemiology in Europe with special focus on increasing our knowledge of the aetiology of different diseases. Our department consists of researchers, doctoral students, biostatisticians, data collectors and database administrators as well as administrative personnel, in total some 250 staff. The department is situated at campus Solna. Further information can be found at ki.se/meb

Do you want to contribute to top quality medical research?

The predictive medicine group is focused on application of machine learning and AI for development and validation of predictive models for biomedical applications, with a particular interest in cancer precision medicine and computational pathology. Our research is based on large and high-dimensional datasets across multiple modalities, including molecular, clinical and histopathology imaging data. Our computational pathology research is based on internationally leading and unique in-house study materials and datasets (>200,000 gigapixel histopathology images and clinical information currently available).

This is a role with a 1 year fixed term contract.

Your mission

This Research Assisitant position includes contributing to development and validation of deep learning models and image processing methodologies for cancer histopathology image data. Machine learning and deep learning techniques are central in this computational pathology project aimed at improving cancer patient stratification.

As a research assistant you will be involved with data management, data pre-processing, and contributing to research activities in the area of computational pathology. The role would suit someone with a MSc in data science or health informatics. Prior skills in python programming, data science, applied statistics and applied machine learning and deep learning is advantageous. The Cancer Histopathology IMage Epidemiology (CHIME) project focus on large-scale studies that include histopathology image data and clinical information. The successful applicant will join an interdisciplinary research team with strong collaboration with clinical departments and other national and international research groups.

Your profile

To qualify, the applicant must hold, or expect to receive, a MSc degree (or equivalent) in a relevant subject and have a strong interest in quantitative aspects of medical research. Prior knowledge in a relevant quantitative field (e.g. machine learning, AI, epidemiology, computer vision, biostatistics, epidemiology) is required.

The applicant should have previous knowledge and experience in programming in Python, and it is desirable that the applicant also has prior experience of working with common deep learning frameworks, such as Tensorflow/Keras or Pytorch.

The applicant should be able to collaborate as part of a team as well as independently and be able to organize and prioritise his/her own tasks with minimal supervision. Excellent written and oral communication skills in English are required.

For further information please contact Associate Professor Mattias Rantalainen (email: mattias.rantalainen@ki.se)

What do we offer?

A creative and inspiring environment with wide-ranging expertise and interests. Karolinska Institutet is one of the world's leading medical universities. Our vision is to pursue the development of knowledge about life and to promote a better health for all. At Karolinska Institutet, we conduct successful medical research and hold the largest range of medical education in Sweden. Karolinska Institutet is also a state university, which entitles you to several good benefits through our collective agreement. And you get to practice freely in our modern wellness facilities, where trained staff are on site.

Location: Solna

Application

Welcome to apply at the latest November 23, 2022.

The application is to be submitted through the Varbi recruitment system.In this recruitment, you will apply with your CV without a personal letter. Instead, you will answer some questions about why you are applying for the job in the application form.

Want to make a difference? Join us and contribute to better health for all