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strategies (e.g., feature attribution, counterfactual explanations, dialogue-based explanations, hybrid symbolic–ML approaches); develop user-facing explanation interfaces that connect algorithmic reasoning
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strong background in mathematics and statistics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience, as well as skilled
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information sources and to provide the relevant analysis of all the available variables in different scenarios conditions. In order to reach this goal, deep learning-based algorithms will be implemented
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training on HMI and methods for classification and comparison of different techniques. Title: Processing and fusion of data coming from a variety of techniques (XRF, HSI, HMI, FUV) for improved material
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will examine the suitability of NIST-standardised post-quantum cryptographic (PQC) algorithms in realistic network settings and explore hybrid approaches that combine classical and post-quantum
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) algorithms in realistic network settings and explore hybrid approaches that combine classical and post-quantum techniques. The project will involve protocol design, system-level evaluation, and performance
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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the Faculty of Medicine and Health Sciences at NTNU. It was established as a Centre of Excellence in 2002, 2012, and again in 2023. Our Centre of Excellence, the Center for Algorithms in the Cortex (https
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collaborators. Tasks include formulating optimisation problems, developing algorithms for optimisation with Bayesian models, and implementing solutions in relevant software. Further tasks include the formulation
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of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test software tools, and