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applications, where we have to deal with detailed and large-scale datasets, often coming from a variety of sources ranging from traditional CAD modelling to 3D scanning. The aim of this research position is to
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model without sharing their personal data; FL reduces data collection costs and protects clients' data privacy. In doing so it makes possible to train models on large datasets that would otherwise have
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Seppelt, Director of the Luxembourg Centre for Socio-Environmental Systems (LCSES) Email: Your profile The successful candidate will apply modern data science techniques, including the analysis of large
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learning, focusing on identifying abrupt shifts in the properties of data over time. These shifts, commonly referred to as change-points, indicate transitions in the underlying distribution or dynamics of a
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research work will be to devise efficient algorithms for source separation in DAS measurements. Issues such as large data volumes that can exceed 1 To per day and per fiber, instrument noise, complex nature
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through text, images, networks, … A similar situation can be encountered in the context of medical data, where the data types may be even more large. It is therefore of strong interest to be able to analyze
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, images, networks, … A similar situation can be encountered in the context of medical data, where the data types may be even more large. It is therefore of strong interest to be able to analyze those
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to perceive their environment because this sensor can produce precise depth measurement at a high density. LiDARs measurements are generally sparse, mainly geometric and lacks semantic information. Therefore
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mouse models (FELASA or equivalent certification); Ability to handle and analyze large-scale omics data; Experience with gene cloning. Soft Skills: Strong sense of initiative, rigor, and motivation
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], which states that random neural networks can be pruned to approximate a large class of functions without changing the initial weights. We are also interested in Neural Combinatorial Optimization, where we