As a Bell Labs Research Intern in the System Architecture and Solution Design (SASD) lab you are conducting innovative software and AI systems research, build impactful solutions that address critical industry needs, and advance the state of the art of the field.

This internship focuses on developing AI-driven solutions to address industry challenges, specifically in the context of data unification, OT system disambiguation, and edge AI deployment.
 

Advanced PhD candidate (3rd year or above) in Computer Science, Electrical Engineering, Physical Chemistry, Optical and Chemical Sensing, or a related field.

Strong background in AI: Expertise in machine learning, deep learning, Gen AI, and related algorithms. Experience with OT systems: Familiarity with industrial control systems, automation and associated technologies including PLCs, DCS, SCADA and HMI, protocols, and data structures Programming skills: Working knowledge of developing industrial control logic and program languages such as Python, R, C++, or other relevant programming languages, and Cloud Native technologies Simulation and implementation skills: Experience with simulation tools and platforms, as well as real-world implementation experience. Excellent communication skills: Ability to clearly communicate research findings and technical concepts in written and oral presentations. Strong analytical and problem-solving skills: Ability to identify and analyze complex problems, develop innovative solutions, and evaluate their effectiveness. Self-motivation and initiative: Ability to work independently and take ownership of research tasks.

The project will involve:

Literature Review: Conducting a comprehensive review of existing AI techniques and solutions for industrial automation, focusing on data unification, OT system interoperability, and edge AI. Solution Development: Designing and developing novel AI-based solutions to address the identified challenges, aiming to improve upon existing baselines. Proof of Concept: Implementing the proposed solutions through extensive simulations and/or real-world proof-of-concept deployments. Presentation & Publication: Presenting the research findings and developed solutions in mid-term and final presentations as well as a publication for peer review or a patent application.

What you will gain:

A comprehensive understanding of the challenges and opportunities in AI-driven industrial automation. A novel AI-based solution addressing one or more of the identified challenges. A validated proof-of-concept demonstrating the effectiveness of the proposed solution. A publication or patent application showcasing the research findings and solution.