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between things, developing the mathematical and computational foundations for optimal information transfer between surfaces and volumetric current distributions in complex scattering media. The research
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
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objectives. Duties of the position The successful candidate will be expected to complete their doctoral education and perform fundamental research, primarily within: Develop and optimize eco-friendly shell
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biotypes; within-target personalized coil positioning, using MRI-guided neuronavigation and network-based metrics to optimize stimulation for each individual; n-of-1 trial design in clinical care, embedding
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. The employment requires strong subject knowledge in optimization, mathematical modeling, and quantitative analysis. You are a problem solver who works well with complex issues, understands complicated written
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related field. - Optimization skills: experience with GAMS or pyomo (or similar), including model formulation. - Life Cycle Assessment (LCA): experience with, e.g., Brightway2, Activity Browser or SimaPro
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system. To achieve this, the PhD will combine: instrumental development (optical optimization, illumination strategies, multi-camera synchronization), optical modeling (contrast mechanisms in phase
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-on experimentation with advanced digital fabrication, numerical modelling, material testing, and process optimization. You will work on the fabrication and mechanical characterization of composite specimens with
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than undesired routes. Solvent and additive effects will further be explored based on mechanistic insights. Your work thus will strengthen the detection and optimization of sustainable chemistry
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Programme (BIED), within the scope of the “HeAICare: AI-Driven Solutions for Optimizing Feto-Neonatal Neurodevelopment: Monitoring, Diagnosis, and Personalized Treatment”, with reference NORTE2030-FEDER