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machine learning (ML) approaches offer a powerful framework for modeling complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal
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extensive electromagnetic modeling to optimize the waveguide structures for minimal loss, efficient confinement, and effective mode matching with external optical components. Particular attention will be
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performance. To validate the achieved tuning range and functional performance in laboratory measurements against modeling and simulation results. This position is part of the MetaTune Doctoral Network
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”) on the development of material flow analysis (MFA) methods and digital methods for spatial analysis. Specific tasks comprise: Design and apply digital models for analyzing and simulating circular futures
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/Qualifications Previous experience in the analysis and simulation of stochastic processes and stochastic differential equation systems. Previous experience with population dynamics models in ecology. Additional
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modeling via CAD software such as COMSOL and HFSS. Design and simulation of electronic instrumentation using Spectre or equivalent and circuit layout using Altium or equivalent. Development of instrument
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, pilot-scale, or high-fidelity simulation data into model calibration and validation workflows Design and run numerical simulations of multiphase flow systems and reactors Quantify model uncertainty and
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this simulator to support the assessment of the FORUM mission performances. Investigating the capabilities of artificial intelligence foundation models for Earth Observation and preparing their integration
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to carry out high-performance numerical simulations using our in-house CFD code, extract physical insights from simplified flow models, and characterise synchronisation thresholds and the robustness
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data, model-based simulation coupling, and uncertainty-aware outputs for lab/clinical validation. Work with 3D datasets, time-series sensors, and multi-omics or assay data to create predictive models and