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research project focused on leveraging deep learning and advanced image processing techniques to improve the current tools for biomonitoring of aquatic ecosystems. This position involves the development and
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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning
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We are seeking a highly motivated PhD candidate with a strong interest or background in AI as well as in one or more of the following areas: Generative AI, Natural Language Processing, Deep learning
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to compare deep neural network and other artificial representations to each other. By applying the new techniques to state-of-the-art architectures, you will test our new methods against existing ones and
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, mMTC and URLLC Machine learning/deep learning techniques and Artificial Intelligence for wireless communications Reinforcement learning, active learning, transfer learning, federated learning and
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, or deep-space exploration. Your research will contribute to advancing sustainable practices in space, including: Developing techniques for debris cleansing and avoidance in autonomous space systems Smart
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I-2503 – PHD IN EXPLAINABLE AI FOR DATA-DRIVEN PHYSIOLOGICAL AND BEHAVIORAL MODELLING OF CAR DRIVERS
, data-science (e.g., neural networks, deep learning, autoencoders, GANs, active learning, etc.); · Knowledge of explainable AI and Knowledge Graphs with ontology (e.g., RDFS, OWL
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. Increase LIST's visibility in the AI field through publications, presentations and participation in relevant forums and conferences. Deep scientific and technical expertise, demonstrated by publications and