2025 U.S Summer Internship Program: Graduate Intern in Machine Learning and Digital Pathology Intern

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Job Description

Takeda has been translating science into breakthrough medicines for 240 years. Every step of the way, our teams have worked together to tackle some of the most challenging problems in drug discovery and development. Today, we’re a driving force behind innovative therapies that make a lasting difference to millions of patients around the world. 

In R&D, all of our history and potential comes together in an environment that welcomes diversity of thought and amplifies every voice. Working closely with colleagues, you’ll play a key role in bringing our rich pipeline of products forward to help patients. Come join a team that’s earned trust for more than two centuries, and find out how advancing transformative therapies at Takeda will shape your bright future. 

​The Computational Oncology group within the Precision & Translational Medicine (PTM) function in the Oncology Therapeutic Area Unit (OTAU) at Takeda has the accountability for driving end-to-end computational innovation and excellence from discovery through development, launch, and beyond as needed to advance our pipeline to patients in need. It consists of talented computational biologists who derive actionable scientific insights from large, diverse, and complex biological datasets including clinical trials and external datasets. They partner closely with teams within PTM and across the enterprise, such as Oncology Discovery, the Data Sciences Institute (including Statistics, Global Evidence and Outcomes, Data Architecture), Clinical Pharmacology, Clinical Sciences, as well as with other computational functions at Takeda as needed. Their collaboration guides robust drug target identification and validation, proof-of-concept in the clinic, and the development of pharmacodynamic and predictive markers to inform data-driven decisions. They also propose actionable solutions to be tested in the laboratory and/or the clinic to identify and advance our innovative cancer therapies. 

 Job Description: 

We are seeking a highly motivated and talented graduate student intern with a background applying convolutional neural networks, autoencoders, or transformer models to solve problems in digital pathology and single cell transcriptomics to join our team. You will work on predicting RNA features from H&E images and fine-tuning single cell foundational models for downstream tasks, contributing to biomarker development, and the advancement of therapies for patients in need. This role includes training deep neural networks, transfer learning and shallow machine learning using H&E images and single cell transcriptomics to understand the tumor microenvironment and predicting therapeutic responses. This internship is designed to immerse you in the forefront of medical research, offering hands-on experience and the opportunity to collaborate with leading industry professionals in a dynamic and collaborative environment. 

How You Will Contribute: 

• Collaborate with internal and external teams to build machine learning models using multi-modal data, including single cell transcriptomics and medical images. 

• Contribute to the dev