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), sensing technologies (fiber-optic sensors, DIC), and computer science (machine learning tools). The aim of this Ph.D. project is to develop a novel bridge monitoring technique based on CLCE coating
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ranges from core areas of computer science and electronics over medical applications to societal aspects of AI. SECAI’s main research focus areas are: Composite AI: How can machine learning and symbolic AI
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, criterion handling and machine learning. Topic The main research objective is to contribute to the development of responsible AI, with a strong focus on trust and confidence handling when dealing with data
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machine learning, deep learning, and network optimization to develop a scalable and secure AI framework for smart transportation. The successful candidate will work with experienced researchers, gain access
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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I-2503 – PHD IN EXPLAINABLE AI FOR DATA-DRIVEN PHYSIOLOGICAL AND BEHAVIORAL MODELLING OF CAR DRIVERS
Master’s degree or Engineer diploma in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field. Experience and skills · Strong knowledge of AI, Machine Learning
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calculated using our Software Energy Lab, which has multiple test machines with GPUs and, in the future, AI accelerators. Development teams currently lack guidance on how to create sustainable systems. You
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application of machine learning algorithms to automatically classify freshwater benthic diatoms at the species level and quantify key morphological traits. These advancements aim to improve ecological
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Mines Paris - PSL, Centre PERSEE | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
-focused learning" or "End-to-end learning". For example, end-to-end machine learning (ML) models can be trained to minimize the downstream decisions regret or even directly learn a mapping from data to
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materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials synthesis and