Connectionists: Postdoctoral research fellow in 3D image analysis of intra-tumor heterogeneity

[Erik Wernersson]

*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).


*Research environment*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.


*Duties*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:

   1. Develop tools for 2D and 3D automatic segmentation of DAPI-stained
   nuclei in z-stacked tissue section scans
   2. 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
   3. 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)
   4. 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).


*Entry requirements*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.

Last application date 31.May.2017 11:59 PM CET

For details and application, please visit
https://ki.mynetworkglobal.com/en/what:job/jobID:143481/where:4/
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