- vision
- language
- time series
- multimodal
- embedded
- internship
- thesis
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Adaptive Granularity Retrieval for Retrieval-Augmented Generation
This thesis explores adaptive retrieval for Retrieval-Augmented Generation, developing a system that dynamically adjusts the granularity of retrieved context (document, section, or passage) based on query intent. The goal is to improve both precision for fine-grained questions and coherence for broad, open-ended queries.
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Graphical User Interface for a Retrieval-Augmented Generation (RAG) App.
Develop a GUI to allow users to interact with a Retrieval-Augmented Generation (RAG) system.
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Deep Learning Approaches to Star Tracker Attitude Determination
Investigation and development of deep learning methodologies for satellite attitude determination using star trackers to overcome traditional limitations in challenging orbital conditions.
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Enhancing Multimodal RAG Systems through Cross-Modal Retrieval and Reranking
This thesis involves the implementation of a cross-modal retrieval and reranking pipeline.
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Machine Learning-Based Corn Yield Forecasting Using Meteorological and Agronomic Data
Develop a machine learning algorithm able to forecast the expected yield at the end of the season by analising weather data and field management data.