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mission, we are in the final stages of developing a prototype for generating daily evapotranspiration products. One of the two selected algorithms, STIC, will be integrated and tested under conditions
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are poised to re-define our future mobility. However, full autonomy is not possible without all-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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for real online scenarios and to study formal verification methods. The program will involve the study of machine learning tools and the development of simulation systems capable of integrating multiple data
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storage, and organic optoelectronics intelligent and advanced polymers and materials. Project description: The Lithium-ion battery comprises multiple individual units known as "cells." Any deterioration
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digital twins using prediction-powered inference to enhance reliability assessment; The theoretical analysis and algorithmic development of methods rooted in statistical learning theory, multiple hypothesis
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-cell and spatial-omics research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic
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will be responsible for programming and maintaining gait exoskeleton systems, developing and implementing real-time control algorithms in C/C++, Python, and Simulink, as well as integrating feedback from
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) sensor data. This will be a small system-on-chip designed to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated