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Field
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networks (5G and WiFi 7 and their evolutions) in terms of architecture, protocols, and optimization. These networks benefit from new technologies and approaches, such as virtualization and AI, to make them
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. Responsibilities: Conduct research in compilation, optimization, and analysis for time-predictable computer architecture. Co-supervise MSc and PhD students. Contribute to teaching and research proposal preparation
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Learning for Biomedical Data. The postholders will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures) to large-scale biomedical
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in professional organizations Basic Qualifications: A PhD in Mathematics, Applied Mathematics, Computational Science, or a related field completed within the last 5 years Preferred Qualifications
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machine learning software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills. The ability to obtain
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terms of research and education, covering all aspects of computer science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering
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/Project: We are building an optimisation-driven framework that (i) makes AI agents reliably operate advanced scientific software (e.g., DFT, Wannierisation, and quantum-transport codes) and (ii) uses
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, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems and security. Job Description We are seeking
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-based architectures, and deployment frameworks for machine learning algorithms. Experience in Deep Learning techniques and solutions used in commercial software development. A track record of publications
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you will need: 1. A completed PhD or a submitted PhD thesis in either computer science, mathematics, computer/software engineering, electrical or electronic engineering, mechanical/mechatronic