Artificial Intelligence
[Open soon]
Work description
The workplan begins with the analysis and preprocessing of historical data, focusing on identifying relevant variables, handling missing and noisy data, transforming textual variables, and storing the data on appropriate platforms. In the second phase, both global models (predicting overall product acceptability) and local models (predicting acceptability for specific consumer segments such as gender or age groups) will be developed using artificial intelligence techniques. Particular attention will be given to managing missing data and ensuring the application of transparent AI, with the goal of supporting decision-making in sensory studies. The final phase involves applying advanced techniques such as transfer learning and ensemble methods to integrate historical and new data. Language models will be used to adapt data formats for consistency. The main expected outcome is a model that is tested and validated in a laboratory environment and prepared for integration into a relevant application setting.
Academic Qualifications
Quality academic background in Computer Science, Computer Engineering, Artificial intelligence, Data Science or equivalent.
Minimum profile required
In conditions that qualify for enrolment in a PhD program.Quality academic background in Computer Science, Computer Engineering, Artificial intelligence, Data Science or equivalent.Knowledge of Machine Learning, Data Science, Databases and LLMs.
Preference factors
Experience in Machine Learning and Data Science. Experience in dealing with prediction with missing data. Python Programming. Willing to do research in Machine Learning, Data Science, Artificial Intelligence and LLM.
Application Period
Since 03 Jul 2025 to 31 Aug 2025
[Open soon]
Centre
Artificial Intelligence and Decision Support