Postdoctoral Fellowship, Pathology - Translational Research & Data Science

Key Skills

Algorithms, Data Analysis, Data Mining, Data science techniques, Java Programming, Machine learning techniques, Mathematics, MATLAB Programming, Python Programming, PyTorch, Scikit-learn, SQL, TensorFlow

Job Description

As postdoctoral fellow in our Pathology group, you will work with a highly collaborative team of scientists in the translational research and data science teams to build an end-to-end phenotypic discovery pipeline for multiplex immunofluorescence (mIF) panels. You will integrate the outcome of the pipeline with other data at Genmab including imaging features, transcriptomics, genomics, and circulating biomarkers to derive new insights within the tumor microenvironment and improve understand of the mechanism of action of immuno-oncology therapies and patient response.

Responsibilities

• Implementation of robust image analysis algorithms to perform cell segmentation, cell phenotyping and neighborhood analysis to interrogate the tumor microenvironment across different mIF panels for different programs.
• Application of machine learning methods to identify relevant phenotypic signatures and spatial features from mIF images to help understand mechanisms of action and clinical response.
• Work closely with colleagues from Data Science to utilize Genmab’s commercially procured database of histopathology images, IHC, genomics, clinical, and transcriptomics data to expand on the spatial features learned from mIF images and build multi-omics models to analyze and predict response to therapy.
• Stay updated on the latest relevant scientific literature, summarize and interpret new findings, prepare manuscripts for publications and present results both internally and at external conferences.

Requirements

• Recent PhD in a quantitative field (e.g. biomedical engineering, physics, data science, mathematics, electrical engineering, or related field)
• Expert knowledge and deep understanding of digital image analysis, feature extraction, pattern recognition, machine learning and data mining methods; application in digital pathology and fluorescence microscopy is a plus.
• Excellent programming skills (Python, Matlab, Java, etc) and experience using common machine and deep learning frameworks (e.g., Pytorch, Tensorflow, Scikit-learn).
• Strong understanding of fluorescence microscopy, whole slide imaging and different microscopic slide file formats.
• A track record of publications and independent contributions to the literature.
• Positive, open and creative mindset to develop innovate image analysis algorithms in close collaboration with pathologists and other scientists.
• Passion for the field of digital pathology and desire to explore new technology platforms related to image analysis.
• Background in immunology and/or immuno-oncology are desired but not required.

Genmab will offer the successful application a challenging position, where the right candidate will have the opportunity to work with highly specialised people across functions in an informal, multicultural environment, with an aim to make a difference in the lives of people with cancer.

At Genmab, we pride ourselves on our unique culture. We are committed to make a positive impact on the lives of cancer patients. We hypothesize and experiment to seek innovative solutions, no matter the employee’s role; we speak up, empower each other, and embrace change and growth; we respect and celebrate our differences while working as one team. Teamwork and respect are central pillars of Genmab’s culture and we therefore ensure an inclusive, open, and supportive professional work environment across our international locations. Genmab employees work with determination, challenge the status quo and cultivate a growth mindset in everything we do.

We are committed to fostering workplace diversity at all levels of the company and we believe it is essential for our continued success. No applicant shall be discriminated against or treated unfairly because of their race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability or genetic information.