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Field
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in Architecture, Architectural Technology, Environmental Engineering, or Building Service Engineering, or in another related
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Energy Modelling”. The PhD candidate will be supervised by supervisors from DTU and Chalmers. This project will integrate energy technology, building physics, HVAC systems, control systems architecture
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interactions and their integration with wider energy networks. Your tasks The focus of this research is to design and develop (physics-informed) hierarchical graph neural network architectures that can capture
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programming skills in Python. You have programming experience in C/C++ or MATLAB (preferred). You have experience with softwarized network stacks or RAN architectures, such as OAI, srsRAN, and FlexRIC
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knowledge of 5G or beyond-5G network architectures (preferred). You are familiar with virtualization, SDN/NFV, containerization, or orchestration platforms (preferred). You have experience with AI/ML
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with a second doctoral researcher to optimize the architecture of fiber sensors, fostering synergistic innovation and breakthrough advancements. Optical fiber-based sensing stands as a cornerstone
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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Materials Group (NANOUP) is focused on controlling the supramolecular assembly of molecules, biomolecules and nanoscale building blocks at the nanometer scale for the design of novel functional architectures
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architecture, including inverted transformers, to create a practical foundation models for time series forecasting focusing on the manufacturing industry. The models will be trained on both large datasets and