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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description https://www.unimib.it/ateneo/gare-e-concorsi
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research . We are seeking a highly motivated and interdisciplinary Research Fellow to
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data. 2. Ability to enter data and update graphs using a computer program. 3. Ability to communicate effectively in both verbal and written form. 4. Ability to remain calm and patient during challenging
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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whole-genome duplication across diverse plant systems (see https://www.yantlab.net/ ). The project is funded through a Formas grant aimed at restoring European ash (Fraxinus excelsior) populations
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
., Scharffe, F., Todorov, K., & Trojahn, C. (2025). Graph Embeddings Meet Link Keys Discovery for Entity Matching. https://doi.org/10.1145/3696410.3714581 [6] Sousa, G., Lima, R., & Trojahn, C. (2025). Results
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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of techniques for fusing LLMs with knowledge graph embeddings for graph-based recommendation in an industrial use case. - Design of experiments involving LLMs on GPU infrastructure. - Analysis and evaluation
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/02/2028 Job Reference Number: 151050-2026-002051 This project develops practical methods for quantified security by constructing attack graphs from knowledge already present in organizational risk
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Dipartimento di Ingegneria dell'Informazione - Università degli studi di Padova | Italy | 2 months ago
analytics, graph learning, and prioritization methods to improve the exploration and readability of high-dimensional networks. Interactive interfaces and complexity-reduction strategies will be developed