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technical excellence at the service of education, research, and innovation. As part of the project led by the SOTERN team in connection with the CREACHLABS grant—which aims to reconstruct, at the network
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Opportunities to work on innovative projects and network globally More information is available at: https://marie-sklodowska-curie-actions.ec.europa.eu/calls/msca-postdoctoral-fellowships-2026
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applications will also be given full consideration in case the positions have not been filled by that date. To apply, follow this link: https://apply.interfolio.com/174821 . If you have any questions, please
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staff position within a Research Infrastructure? No Offer Description This research project aims to develop a synthetic dataset generation technique to optimize the training of neural networks (NNs
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), Multilayer Perceptron (MLP), Autoencoders, Convolutional Neural Networks (CNNs), and Kolmogorov–Arnold Networks (KANs). Desirable knowledge of Gradient Boosting models such as HistGBM, LightGBM, and XGBoost
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the HDS-LEE graduate school Your Job: Develop methods and workflows to construct robust co-regulation networks from large single-cell and spatial transcriptomics datasets Integrate ontologies and metadata
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Job Posting Title: Network Analyst ---- Hiring Department: Enterprise Technology - Infrastructure ---- Position Open To: All Applicants ---- Weekly Scheduled Hours: 40 ---- FLSA Status: To Be
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Related works: [1] Sagawa et al., Distributionally Robust Neural Networks for Group Shifts (GroupDRO) https://arxiv.org/pdf/1911.08731 [2] Liu et al., Just Train Twice: Improving Group Robustness Without
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the EUARTHURS: European Arthurs, Medieval to Modern Marie Skłodowska‑Curie Doctoral Network Ref: 101226326 (2026-30). These posts form part of a major EU-funded international programme involving partners in Wales
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the network, as well as with academic and non-academic partners. General information about the CLIMES project is available at: [https://www.climes.se/climesdn/ ](https://www.climes.se/climesdn/ ) All working