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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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collaboration with national and international partners. In addition, there will be plenty of opportunities to collaborate with fellow postdocs and PhD students at the Bioinformatics chair group in Wageningen
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of the algorithms developed in this project. About you The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance
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collaboration with national and international partners. In addition, there will be plenty of opportunities to collaborate with fellow postdocs and PhD students at the Bioinformatics chair group in Wageningen. You
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forefront of hydrogen technologies and their integration to the energy systems. The successful candidate will hold a PhD degree (or close to completion) in Mechanical/Electrical Engineering or related subject
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on the data recorded in the team, you will develop and test machine learning algorithms for perovskite tandem solar cells' energy yield and degradation Data cleaning and preparation Assisting integration
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Location: Luxembourg, Luxembourg 1511, Luxembourg [map ] Subject Area: Quantum LDPC codes with efficient decoding algorithms Appl Deadline: none (posted 2025/08/05) Position Description: Apply Position
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 11 days ago
, located at Inria Paris and École Normale Supérieure (ENS). The team conducts research in various aspects of quantum information theory, including quantum error correction, quantum algorithms, and the
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation