Job Description The Vertex Internship Program offers students the opportunity to learn about the pharmaceutical industry from a hands-on perspective. You will be working closely with and learning from some of the brightest people in the industry. When you join our internship program you should not be surprised to see that our environment is one that is innovative and team oriented, where collaboration is not just a word, and "we are relentless" is in everything we do. Students can expect to be challenged by their project plan, connect with other interns,and feel valued by the Vertex community. Not only will interns learn about their day-to-day tasks through their specific work assignments, but they will interact and have exposure to our organizational leaders and their teams. Job Summary: We are seeking a computational scientist/biologist to join Vertex for an internship experience this summer in the Data and Computational Sciences department. We are looking for exceptional candidates to assist with early-stage research on active drug discovery programs. The candidate will join the Data and Methods - Image Analytics group and focus on analysis of high content imaging data to enable off-target identification and understanding of compound mechanism of action (MoA). The project will include both method development and data analysis aspects. The key tasks for this position will be to explore and apply novel methods for improving morphological phenotypic characterization of compound target identification in the pursuit of developing cutting edge, transformative therapies. Key Responsibilities: The candidate will be expected to work with their mentor to explore methods, develop and validate analytical workflows and perform relevant data analyses In particular the project will be focused on evaluating the Cell Painting assay for in house compound libraries and novel compounds. In particular the candidate will have the opportunities to explore and apply methods: For assessing quality of morphogogical phenotypic profiles, to extract deep learning features from cell painting data, to compare the performance of image features, for clustering and comparison of compound phenotypes, off-target or mechanism of action (MoA) analysis of compound libraries The candidate is expected to implement experiments and analyses using relevant programming languages and/or software tools * Following guidance from their mentor, the candidate must have the ability to work independently and as part of a team
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