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and spoken English. Desirable: Experience with photonic/electromagnetics design software. Familiarity with deep learning platforms (e.g. TensorFlow, PyTorch). Funding and eligibility The project is
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07.04.2026, Academic staff PhD position at the interface of computational physics, machine learning, and experimental reactor design. The project focuses on developing PINN-based simulations
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computer scientists to design paradigms that compare "active" learning (standard VR) against "proprioceptive" learning (haptically guided movement), measuring outcomes such as path efficiency, force
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Be part of the future of creativity support for professionals at Aalborg University! This interdisciplinary project investigates how generative AI can be incorporated into the building design and
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-making, and explainable reinforcement learning for large-scale public-health applications. You will: develop new multi-criteria reinforcement learning algorithms for complex decision problems: you design
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new data becomes available, while preserving previously acquired knowledge. A key objective is to design signal representations and learning mechanisms that enable stable adaptation without forgetting
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. (Home applicants only). In this Ph.D. project, you will advance this research field by investigating how to develop, design, and evaluate domain specific multi-agentic AI models and systems that can plan
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lab researchers in computer science, policy, economics, and related disciplines to design, implement, and analyze state-of-the-art machine learning and research computing approaches. The Machine
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optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter
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requirements, please see the subject’s general study plan . We are looking for candidates with: a strong interest in developing new methods for image processing, computational mathematics and machine learning