Karolinska Institutet, Department of Medical Biochemistry and Biophysics, Translational Medicine and Chemical Biology
A postdoctoral position in 3D image analysis of intra-tumor heterogeneity is immediately available in the Crosetto lab for Quantitative Biology and Technology (https://bienkocrosettolabs.org/) with the goal of studying phenotypic, genetic, and transcriptional intra-tumor heterogeneity by high-throughput microscopy imaging of serial tissue sections from different tumor types and hundreds of patients. The position is funded through a 33 million SEK research grant (Integrated Visualization of Intra-Tumor Heterogeneity) recently awarded to Dr. Crosetto by the Swedish Foundation for Strategic Research (SSF).
The Crosetto lab is part of the Science for Life Laboratory (SciLifeLab) situated at the Karolinska Institute Solna campus. SciLifeLab is an interdisciplinary center for molecular biosciences with focus on health and environmental research, bringing under the same roof groups from four universities: Karolinska Institutet, KTH Royal Institute of Technology, Stockholm University and Uppsala University. The center features state-of-the-art technology platforms, including next-generation sequencing, high-throughput histology, super-resolution microscopy, proteomics, image analysis, and bioinformatics.
The successful candidate will join an interdisciplinary and dynamic team of international researchers, including clinicians, biologists, biotechnologists, engineers, computer scientists, and physicists. Our mission is to transform the way we understand complex biological phenomena and diseases such as cancer, by integrating next-generation sequencing technologies, single-molecule microscopy methods, and advanced computational tools.
The main goals of the project are to develop image-based metrics of phenotypic, genetic, and transcriptional intra-tumor heterogeneity in various cancer types and clinical samples, and to assess whether these metrics are predictive of clinical endpoints such as response rate and overall survival.
Specific tasks of the position will include:
- Develop tools for 2D and 3D automatic segmentation of DAPI-stained nuclei in z-stacked tissue section scans
- Develop deep learning approaches (convolutional networks) to automatically identify different cell types (tumor cells, stroma, blood vessels, etc.) in the images analyzed, with particular emphasis on identifying different immune cell types
- Apply spatial statistics methods to study the spatial distribution of different cell types, and define metrics of intra-tumor heterogeneity to be correlated with clinical endpoints (response rate, survival)
- Use 3D image data to construct high-resolution maps of the intra-tumor vasculature and model tumor growth
The successful candidate will be jointly supervised by Dr. N. Crosetto (supervision on the biological and medical part of the project) as well as by Dr. K. Smith, Director of the BioImage Informatics national facility at SciLifeLab Stockholm (supervision on the image analysis part).
A person is eligible for a position as postdoctoral research fellow if he or she has obtained a PhD no more than seven years before the last date of employment as postdoc.
The successful candidate shall hold a PhD in computer science and/or physics and/or mathematics and clearly demonstrated prior experience in image processing, machine learning, and statistical analysis (not necessarily for biological applications). Proficiency in various programming languages (C++, Python, Matlab, bash) and knowledge of software engineering principles (code optimization, parallel computing) is mandatory. Familiarity with web applications design and visualization experience is a plus. Prior use of Matlab and/or Python for image analysis and familiarity with a deep learning framework (Tensorflow, Caffe, Torch) is highly desirable. Candidates with demonstrated expertise in biostatistics are particularly encouraged to apply. A strong motivation to work in an interdisciplinary and collaborative environment, and a strong sense of mission and self-drive are indispensable.
More information at https://ki.mynetworkglobal.com/en/what:job/jobID:143481/where:4/
Last application date 30.Apr.2017 11:59 PM CET