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.

Role Responsibilities

• Utilize 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.
• Perform 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
• Quantify 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.
• Develop 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
• Be 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

Qualifications

Education and Experience

• PhD 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
• Proven record of scientific rigor and scientific success
• Ability to work effectively on teams and good team player attitude required
• Excellent communication skills (oral and written) as demonstrated by publications & presentations
• Ability to multi-task and project prioritization required

Technical Skill Requirements

• Experience 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.
• Ability 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
• Ability to identify and discern patterns and insights within structured and unstructured data.
• Ability to work closely with bench scientists to troubleshoot and solve potential imaging artifacts
• Excellent programming experience in machine learning and deep learning (C++, Python/Perl, R, Matlab)
• Authorship demonstrating the application of image analysis in high quality publications

Preferred Qualifications

• Demonstrated understanding of biology (specifically immunology) desirable
• Experience 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
• Proficiency in developing web-based applications and ability to code in Java desirable