20 nano-patterning PhD positions at NTNU Norwegian University of Science and Technology
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PhD candidate in AI-driven ship design, connected to the newly established Norwegian Maritime AI Centre . The position aims to advance how artificial intelligence support ship design processes
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NTNU and the application process here. About the position The aim of this PhD project is to develop explainable physics-informed RNNs for autonomous navigation and neural observer design within
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with the nano- and microfabrication equipment in the clean room at NTNU NanoLab, and the NORTEM TEMs. Of special interest is the capabilities of the state-of-the-art NORTEM ARM300CF, which was installed
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) section. The BEE section investigates ecological and evolutionary patterns and processes underpinning biodiversity, scaling from genes to communities and ecosystems, and how these are affected by
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of this, computational thermodynamics can be a valuable tool to enhance the design of the process, reduce experimental trials, and increase the yield of the recycling process. Furthermore, it is also crucial for achieving
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Theory and Principles for Receiver Design This PhD project aims to develop a theoretical framework for biocommunication using semantic communication theory and principles. The foundational framework will
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but not limited to Generative AI and multi-agent ecosystems. The approach followed encapsulates Design-Based Research (DBR) and Responsible Research and Innovation (RRI) principles. The PhD fellow will
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. Collaborating closely with SINTEF and industrial partners (Hydro and Benteler) to provide input for alloy design and processing strategies. The work will be done at the Trondheim node of NORTEM/TEM Gemini Center
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. The approach followed encapsulates Design-Based Research and Responsible Research and Innovation principles. The PhD fellow will engage with designing and evaluating user-centred AI artifacts that support
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doctorate. Carry out research of good quality within the framework described above. To explore, design and experiment with different techniques for fairness in recommender systems in the job domain. To take