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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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sciences, field of information science for the group of subjects related to data structures and algorithms at the Department of theoretical and applied foundations of information sciences Where to apply E
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 16 days ago
accurately study the selected algorithms, participate in the development and maintenance of the PEPit (https://pepit.readthedocs.io/ ) and AutoLyap (https://github.com/AutoLyap/AutoLyap/ ) software packages
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related to staff position within a Research Infrastructure? No Offer Description The Leverhulme Centre for Algorithmic Life is a major new interdisciplinary research hub dedicated to the study of how
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on utilization of less known inverter topologies (e.g. Dual inverter, 4 bridge inverter) with modern semiconductor devices (GaN, SiC) The aim is to implement own designed algorithms in a signal processor and
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algorithms into electron impact ion sources as well as tackle changes over time in ISOL systems, to ensure an optimal tuning at any point in time during operation. Where to apply Website https://www.sckcen.be
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-years project. You will work on a project related to algorithmic fairness in cardiometabolic disease prediction and care at the Copenhagen Health Complexity Center, Department of Public Health, Faculty
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responsible for the end-to-end investigation of novel federated learning strategies for causal inference. The role will bridge rigorous theoretical work with hands-on algorithm design and development on real