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Field
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potential research topics to improve the project results. Research topics may include interventions in public services, labor markets, marketing, and digital platforms, using large-scale data and algorithmic
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implementation; b) monitor the project implementation details; c) develop control algorithms and digital twin models; d) guide prototype development and laboratory testing; e) analyse test data and
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areas: EEG, multimodal MRI, or imaging genetics, documented through peer-reviewed publications excellent programming skills (e.g. Python, MATLAB, R) experience working with distributed computing resources
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bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms Downloading a copy of our Job Description Full
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-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements. § Implementing algorithms
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of physics simulations in parallel, distributed among multiple computing nodes. Legislation framework: Research Fellowship Holder Statute, in accordance with Law 40/2004, of 18 August, in its current
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or use existing simulation platforms to validate the developed algorithms and models. Analyse simulation data, and create visualizations to support research findings. Design and build prototypes
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. In this role, you will be part of the research team, working to develop and evaluate privacy-preserved Generative AI algorithms for generating synthetic Personal Identity Information (PII). This aims
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of Earth and Environment (SEE), and working closely with its School of Civil Engineering, you will use various seismic datasets – including data already recorded with fibre-optic distributed sensing systems
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technology, from neural networks, distributed reinforcement learning to agentic AI and recent developments in this rapidly evolving field, Good personal properties and genuine willingness to collaborate within