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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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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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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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. Solid background in signal processing, statistics, or computational neuroscience, preferred. Outstanding presentation skills, preferred. Experience in graphing, statistical analysis and data management
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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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prepares graphs and charts for management use or presentations about departmental or divisional activities. • Coordinates and arranges meetings, conferences and committee meetings. • Makes complex travel
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Dipartimento di Ingegneria dell'Informazione - Università degli studi di Padova | Italy | 3 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
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, such as books and calculators for students. Check out and maintain laptops, cameras, LCDs, and other equipment for faculty use. Demonstrate the use of calculators, both graphing and non-graphing, and any
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Programme? Not funded by a EU programme Reference Number AE2026-0234 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https
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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