Data Classification and Machine Learning- MSc and PHD-Summer internship 2025- Research Lab

Introduction

At IBM Research, we believe that work is more than just a job—it's a calling. We're seeking innovative minds to join us in pushing the boundaries of what's possible in artificial intelligence and drug discovery. Our mission is to build, design, code, and collaborate on groundbreaking projects that have the potential to solve some of the world's most challenging problems in healthcare and life sciences.

Our summer internship program offers an opportunity to collaborate with IBM’s leading research team for a 3-month, full-time internship (5 days a week) at our labs in Haifa or Tel Aviv.

Your Role And Responsibilities

Join the Data Security and Privacy group in IBM Research - Haifa to work on cutting-edge technologies for data classification powered by machine learning.

Our team focuses on creating innovative techniques for sensitive data discovery, classification, and contextual analysis, leveraging state-of-the-art machine learning methods. We tackle real-world challenges such as identifying and classifying personally identifiable information (PII) and other sensitive data types, building scalable data classification pipelines, and integrating these solutions into enterprise-grade data security frameworks.

You Will Have The Opportunity To

• Develop novel classification algorithms for structured and unstructured data.
• Work on enhancing existing machine learning models to improve accuracy in data labelling and classification tasks.
• Contribute to the design of tools that map data classification outputs to actionable business insights, supporting privacy and security objectives.
  
  

• MSc or PhD student in Computer Science, Data Science, or a related field at a leading university.
• Strong theoretical knowledge of machine learning and deep learning techniques.
• Hands-on experience with Python and ML/DL frameworks (e.g., PyTorch, scikit-learn).
• Practical experience in using and fine-tuning transformer-based models (e.g., BERT, RoBERTa) and large language models (LLMs) for various applications.
• Proficiency in data manipulation and analysis using libraries like Pandas and NumPy.
• Knowledge of model evaluation techniques, including metrics for classification and fine-tuning performance.
  
  

• Experience with sensitive data classification or entity extraction.