Computer Science
Work description
- Development of model/process chains that allow AI-based assistants to support human operators' decisions under risk and model uncertainty, and considering human-AI joint learning. - Develop methodologies to assess the robustness and safety of AI-assisted human decisions, hybrid AI-human co-learning, and fully autonomous AI, considering risk assessment in line with the 4) EU AI Act, as well as quantified reliability and robustness by providing guidance on how to create and use ‘adversarial’ datasets. - Validate the methodologies developed on real data and open-source simulators for power grid use cases. - Disseminate the work in national and international journals and/or conferences.
Academic Qualifications
Degree in Computer Science, Informatics of similar area
Minimum profile required
Past experience (or academic training) in Artificial Intelligence, algorithms with a focus on traditional machine learning (shallow learning) and deep learning methodologies. Knowledge of Data Science, including the development of data analysis and visualisation pipelines.
Preference factors
- Knowledge of Python programming. - Development of IT tools for use in real environments. - Knowledge of simulation methodologies.
Application Period
Since 25 Oct 2024 to 08 Nov 2024
Centre
Artificial Intelligence and Decision Support