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advance research in computer vision, machine learning, and/or robotics for the digitalization, monitoring, and automation of civil infrastructure. The role will focus on developing innovative methodologies
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and TCNs, for safety assessment and accident prediction. Process and analyse multi-source traffic data for model training and validation. Publish technical findings in top-tier journals. Job
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Università degli Studi di NApoli Federico II -Dipartimento di Medicina Molecolare e BiotecnologieMediche | Italy | 2 months ago
16 Jan 2026 Job Information Organisation/Company Università degli Studi di NApoli Federico II -Dipartimento di Medicina Molecolare e BiotecnologieMediche Research Field Biological sciences » Biology Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher...
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gangen du var inne på nettsiden. Cookien slettes automatisk etter tidens utløp. Nedenfor kan du kan se en komplett liste over cookies. Slik avviser eller sletter du cookies Du kan til enhver tid avvis
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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. Qualifications: Familiarity with machine learning interatomic potentials, CPU and GPU parallelization, knowledge of LAMMPS and molecular dynamics, experience with first principles calculations of dielectric and
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contribute to the CELECT work package on AI-Assisted Engineering. The research will focus on developing AI-assisted methods for parallel hardware-software co-design of mechatronic systems, enabling virtual
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will play a key role in building a parallelized, agent-driven exploration system and integrating a multimodal detection pipeline, ensuring real-time performance, scalability, and deployment readiness in
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/FYP students Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent Independent, highly analytical, proactive and a team player Excellent
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the group can test and validate models of sodium-ion battery behaviour. As such, proficiency in computer programming (particularly python) is required as well as knowledge of numerical methods