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PRODID:-//Cancer Epigenetics Society - ECPv5.3.2.1//NONSGML v1.0//EN
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X-WR-CALNAME:Cancer Epigenetics Society
X-ORIGINAL-URL:https://ces.b2sg.org
X-WR-CALDESC:Events for Cancer Epigenetics Society
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DTSTART:20180325T010000
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DTSTART:20181028T010000
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DTSTART;VALUE=DATE:20180322
DTEND;VALUE=DATE:20180424
DTSTAMP:20260729T212240
CREATED:20180322T093847Z
LAST-MODIFIED:20180322T094520Z
UID:19810-1521676800-1524527999@ces.b2sg.org
SUMMARY:Quantitative Image Analyst - Immuno-oncology
DESCRIPTION:We seek a highly motivated quantitative Image Analyst to be part of a larger inter-disciplinary team that focuses on target discovery\, validation and biomarker research in immune-oncology. The successful candidate should have deep knowledge of image analysis methods and know how to apply them towards a better understanding of tumor immune microenvironment in response to various immunotherapies. The image analyst will work closely with immunologists\, biologists\, computational biologists and translational oncologists\, to support a diverse set of research programs and should enjoy operating in a highly dynamic and cooperative environment. The candidate should have the ability to work independently and come up with quick\, robust and creative computational tissue image analysis solutions to various research goals. \nRole Responsibilities \n\nUtilize and develop cutting edge image analysis techniques that accurately QC\, segment\, register\, and quantify digital histopathology images\, to understand spatial changes in the tumor immune microenvironment and ultimately predict biomarkers in response to various cancer immunotherapies.\nPerform morphological immunophenotyping of tumors using deep understanding of statistical and predictive modeling concepts\, machine-learning approaches including but not limited to deep learning\, clustering and classification techniques\, and image segmentation strategies\nQuantify and interpret highly multiplex immunofluorescent images on tissue samples from preclinical models and human subjects\, to define the phenotype\, functionality\, and localization of immune and other cells within complex tissue types in both 2D and 3D settings.\nDevelop innovative algorithmic and statistical approaches to integrate diverse datasets (IHC/IF images\, FACS/cyTOF\, RNA-seq\, DNA-seq\, epigenetics) from preclinical models and clinical trials to identify targets\, biomarkers\, resistance mechanisms of current therapies\, and predict effective therapeutic combinations\nBe up-to-date on state-of-the-art methods and techniques in computational image analysis and provide support on an as-needed basis to cross-disciplinary project teams\n\nQualifications \nEducation and Experience \n\nPhD in Bioinformatics/Computational Biology\, Immunology\, Mathematics\, Statistics\, Biostatistics or closely related field with 8+ years of post-PhD experience with substantial expertise in using advanced computational image analysis platforms for image processing\, analysis\, and visualization across various tissue architectures\nProven record of scientific rigor and scientific success\nAbility to work effectively on teams and good team player attitude required\nExcellent communication skills (oral and written) as demonstrated by publications & presentations\nAbility to multi-task and project prioritization required\n\nTechnical Skill Requirements \n\nExperience in applying and developing state-of the-art image analysis techniques and methods on images from latest immunohistochemistry techniques (multiplex IF/IHC\, ISH) across various tissue architectures.\nAbility to categorize and analyze data sets via neural networks and associated deep learning technologies (including but not limited to TensorFlow library) facilitating quantitative processing and interpretation of highly multiplex immunofluorescent images\nAbility to identify and discern patterns and insights within structured and unstructured data.\nAbility to work closely with bench scientists to troubleshoot and solve potential imaging artifacts\nExcellent programming experience in machine learning and deep learning (C++\, Python/Perl\, R\, Matlab)\nAuthorship demonstrating the application of image analysis in high quality publications\n\nPreferred Qualifications \n\nDemonstrated understanding of biology (specifically immunology) desirable\nExperience in analysis of large-scale genomic data such as RNA-seq\, Exome-seq\, whole-genome seq\, ChIP-Seq\, genotype\, microarrays and flow/mass cytometry desirable\nProficiency in developing web-based applications and ability to code in Java desirable
URL:https://ces.b2sg.org/event/pfizer-22mar18/
LOCATION:Pfizer – Pearl River\, NY\, 401 North Middletown Road\, Pearl River\, NY\, 10965\, United States
CATEGORIES:Jobs
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