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revisit discretization methodologies in view of modern requirements and computational capabilities. The candidate will focus on developing mesh generation algorithms meeting the following criteria
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and interpretation. Prominent examples include time sequences on groups and manifolds, time sequences of graphs, and graph signals. The objectives The project aims to develop unsupervised online CPD
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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global
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immune cells in Alzheimer’s disease. Our lab investigates how immune cells and signals shape brain development, activity, and aging. In this project, we aim to dissect the contribution of adaptive immune
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wishes to develop work involving behavioural/experimental economics, in collaboration with energy economists. To this end, we are looking for a research associate who can help to drive forward this new
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, you will be a member of a team of 8 to 10 energy system modelers. You will contribute to the development, maintenance and use of our linear programming-based dispatch and unit commitment model of the EU
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] and taphonomy of animal bones [Cifuentes-Alcobendas and Dom´ınguez-Rodrigo, 2019] are gradually intensifying. Thus, the present PhD project is an opportunity for the development of original ML solutions
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the Director of DMI in translating strategic vision into effective day-to-day IT execution. Develop and implement a long-term IT operations strategy aligned with DMI’s missions, LIH scientific goals and
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only to healthy knee bone structures in the context of elective orthopedic surgery [5, 6]. Moreover, many of these models are developed by private companies for implant placement, limiting accessibility
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This post-doc aim to develop an analytical method for the quantification of the long-lived radionuclide Selenium 79 in nuclear waste. More specifically, this will be done using preparative