The use of AI components in safety-critical aviation systems brings various benefits in terms of performance and enables new functionalities that are not possible to implement in traditional software. Such systems often require certification from global aviation authorities and, therefore, must exhibit a high level of trust and guarantee on the absence of unintended behavior. This is specifically challenging for Machine Learning (ML) based systems (including Deep Learning and Reinforcement Learning), since ML models, such as neural networks, are often complex and black box. In the context of AI Trustworthiness and Certification in Aviation Applications, several internships/theses opportunities are available at Collins Aerospace, such as:
Optimization Methods for More Explainable and Verifiable AI/ML
Ensuring Safety of Adaptive Learning Systems
Trustworthy AI Solutions for Prognostics Applications
30% theory, 50% software, 20% testing
Requirements: Python
Contacts: Dmitrii Kirov, Marco Roveri, Giovanni Iacca
There are various topics available at Oracle Labs. Topics of interest include (but are not limited to):
Automated Machine Learning with Explainability
Graph Machine Learning
Machine Learning for DBMS and Data Integration
Large Language Models for Semantic Search, Code Generation, and Assistants
Generative AI and AI Agents
10% theory, 70% software, 20% testing
Requirements: depending on the project
Contacts: Giovanni Iacca (will forward to the relevant team at Oracle)
There are various topics available at Biocentis, a company working on pest control. Topics of interest include:
Development of PesTwin GUI
Graph generator
Model calibration
Optimal release strategy
Automatic egg counter
10% theory, 70% software, 20% testing
Requirements: see details here.
Subjects: machine learning, optimization, web development, data science
Contacts: Matteo Rucco, Giovanni Iacca
There are various topics available at Aida Innovazione, a company working on AI and IoT applications. Topics of interest include:
Smart cities: optimization of traffic flows within car parks
Predictive maintenance for smart buildings (monitoring, control, and forecasting of plant energy consumption)
Fault/anomaly detection, preventive maintenance
Explainable AI for intelligent plant management
10% theory, 70% software, 20% testing
Requirements: Python
Subjects: machine learning, optimization
Contacts: Luca Cornali, Giovanni Iacca