AbbVie's Convergence AI and Data Analytics (CADA) team leverages advanced AI and ML to integrate and analyze diverse data sources, aiming to develop innovative AI solutions that drive scientific discovery and accelerate drug development. The specific problem this role addresses is developing models capable of predicting causal relationships between genes and diseases using graph machine learning.
Requirements
- 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
- 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
Responsibilities
- 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
- Implement these architectures in Python
- Perform necessary data pre-processing
- Train and rigorously validate the models
Other
- 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
- Relocation support for eligible students
- Opportunity to work on cutting-edge machine learning problems with real-world implications for human health
