Intern Program - Research - Software Engineering

Introduction
At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.

Your Role and Responsibilities
IBM Research Brazil is seeking motivated interns to conduct applied research on Software Engineering (SE) in Sustainable Material Discovery. The goal of this position is to research and develop innovative services and tools to help subject matter experts on generating and evaluating their hypotheses.
The intern will work on existing projects which have a well-defined agenda, deliverables, and schedules. Those projects

can be related to Machine Learning, Knowledge Engineering and Knowledge Integration. Moreover, the intern is going to acquire experience working with interdisciplinary research teams at IBM Research Brazil and IBM worldwide. At IBM, we encourage taking internal courses and attending talks/seminars to improve skills in key areas. The intern will, therefore, be able to learn, apply the acquired knowledge in real scenarios, produce useful assets and move forward the state of art through academic writing.

Required Technical and Professional Expertise
• Master students that started the masters in 2024 or Doctoral students that started the course in 2023 or latter.
• Enrolled in Graduate (Master or Doctor) in Computer Science, Computing Engineering, Information Systems or equivalent;
• Experience in programming languages, such as, Python (preferable), Java, and Node.js;
• Commitment with software engineering good practices;
• Experience in writing scientific papers and/or technical reports is a differential.

Preferred Technical and Professional Expertise
Intermediate English.
Familiarity with knowledge representation, knowledge reasoning, machine learning and data integration.