2026 Graph Machine Learning Intern (PhD)
AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.
Job Description
Envision spending your summer working with energetic colleagues and inspirational leaders, all while gaining world-class experience in one of the most dynamic organizations in the pharmaceutical industry. This is a reality for AbbVie Interns.
The Convergence AI and Data Analytics (CADA) team at AbbVie leverages advanced artificial intelligence and machine learning techniques to integrate and analyze diverse data sources throughout the organization, including biological datasets, clinical trial results, real-world evidence, and genomics. We partner closely with scientists across AbbVie to develop and apply innovative AI solutions that drive scientific discovery and accelerate the advancement of AbbVie’s drug development pipeline.
We are seeking a highly motivated and innovative intern who can employ cutting-edge methods in graph machine learning to develop models capable of predicting causal relationships between genes and diseases. They will review published literature on graph neural networks to identify relevant architectures, implement these architectures in Python, perform necessary data pre-processing, and train and rigorously validate the models.
The generated predictions will play a critical role in advancing AbbVie's drug development efforts, with significant implications for a wide range of human diseases.
Key responsibilities include:
- Designing, training, and evaluating graph machine learning models to predict trends in biomedical research
- Developing and optimizing data pipelines for graph data processing and model training
- Reviewing prior literature to identify suitable machine learning approaches and architectures
- Communicating findings and insights to cross-functional stakeholders
Qualifications
Minimum Qualifications
- Currently enrolled in university, pursuing a PhD in computer science, machine learning, bioinformatics, mathematics or other related field
- Must be enrolled in university for at least one semester following the internship
- Expected graduation date between December 2026 – July 2027
- Knowledge of fundamental machine learning concepts
- Proficiency in Python, including data manipulation libraries such as Pandas and NumPy
- Experience building machine learning models in a major framework such as PyTorch or TensorFlow
Preferred Qualifications
- Familiarity with knowledge graph data
- Experience with graph machine learning, including frameworks such as PyTorch Geometric or Deep Graph Library (DGL)
- Working knowledge of SQL