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and engineer sequence-activity genome-wide. The project will focus on using model systems and aim to produce complete maps of how mutations affect fitness and how these mutations interact within and
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the following research problems in their PhD theses: 1) Interpretable machine learning for network adaptation. In this thesis, the student will study how interpretable models and explainable learning
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design and modelling, and numerical modelling and electromagnetic mathematical methods: RCWA, FDTD. Photopolymer-based holographic materials. Full-colour holography. Vision optics. Programming in MATLAB
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. The recruit will focus their work on one or more of the following sub-lines of work: low-resolution algorithms, model optimization with HPC techniques, edge computing, quantum computing, energy-efficient
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availability. Expertise in methodological approaches to develop empirical and mechanistic models. Advanced user of Statistical analysis software. Languages: Proficiency in Catalan and Spanish. Proficiency in