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Field
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout
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-tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to
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-driven proteomic. Who are we looking for? A motivated researcher with strong programming skills and knowledge of machine learning algorithms, bioinformatics, proteomics, and molecular biology, keen to
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reliable algorithms for estimating the aforementioned parameters on a global scale will enable the implementation of operational services in precision agriculture and forest management. This PhD project will
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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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. Knowledge on multiphase (gas-particle two phase system), thermal energy storage, and/or renewable hydrogen technologies. Familiar with application of machine learning and deep learning algorithms to fluid and
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· developong new genetic algorithms related to finite element (FE) modeling · presentation of results and their disseminatio The successful candidates must be enrolled in PhD School program
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(IMI, IMT, IRS, Census) with descriptive methods and causal econometric techniques. It will use various approaches to identify vacant dwellings, including machine learning algorithms to visually detect
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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, or spatial relationships of objects—and to indicate when it is unsure about its input. Key expected outcomes include the creation of monitoring algorithms that identify early signs of performance issues, and