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- Delft University of Technology (TU Delft); Published 11 Nov ’25
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Field
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theoretical foundations of modern learning algorithms. The position will be supervised by Florence d’Alché-Buc, Charlotte Laclau, and with Rémi Flamary and Karim Lounici from École Polytechnique
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nuclear/high-energy physics, quantum information science, lattice gauge theories, quantum many-body dynamics) Experience: Preferred--computational experience in simulating/implementing quantum algorithms
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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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, telecommunications or related field. Other requirements include Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design
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inference in engineered systems, including telecom networks; The development of neuromorphic algorithms and spiking neural models with built-in efficiency and reliability guarantees; The design of reliability
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wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences
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intersection of human-robot interaction and manipulation in healthcare settings. The project involves developing algorithmic solutions for an optometry application where robots assist optometry patients
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At the Faculty of Engineering and Science, Department of Materials and Production one or more Postdoc positions in the area of Optimization and Algorithm Design are open for appointment from April
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University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including bulk and single
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 2 months ago
that diffusion models are a fundamental divergence from traditional deep learning paradigms. This suggests that existing generalisation theories are insufficient and highlights the need for a bespoke, algorithm