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Field
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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through simulation Utilize advanced CMOS technology nodes (28nm, 22nm, and below) Automate the design and layouts using Skill programming Develop behavioural models for circuit verification Contribute
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simulation : Aspen HYSYS/Plus. • Optimization skills: experience with GAMS or pyomo (or similar), including model formulation. • Life Cycle Assessment (LCA): experience with, e.g., Brightway2/SimaPro/Activity
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of “Developing locally integrated models for sustainable energy investments in rural areas”. The sustainable transition of Europe’s energy infrastructure involves large investments in new infrastructure, such as
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to ensure robust and efficient energy conversion in challenging environments. The work will combine analogue/mixed-signal IC design, system-level modelling, and experimental validation of fabricated
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning
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and quantitative sustainability assessment methods, including Life Cycle Assessment (LCA), Energy System Analysis (ESA), and socio-economic modeling. The candidate will be expected to contribute
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experimental tests Skilled in analyzing, post processing and interpreting test results Experienced in numerical modeling and simulation techniques Has published papers in peer-reviewed journals Experienced in
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participation in six international training weeks, and international secondments with academic and non-academic partners. The PhD position will focus on the topic of “Developing locally integrated models
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-efficiency converters based on SiC/GaN wide-bandgap devices and resonant topologies to achieve > 98% efficiency. You will design, simulate, and experimentally validate DC-DC and DC-AC converter prototypes