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Field
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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The Modelling & Theory Group (GMT) at SPEC is seeking a permanent researcher in the area of condensed matter theory, with a focus on numerical methods in nano- to mesoscopic quantum physics. Job
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the development of numerical methods for astorphysical fluid dynamics and radiation transport. Projects may employ a range of approaches from analytic modelling and numerical calculations on desktop
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in the numerical modeling of crack initiation, crack propagation and fracture in CCAs prospective for hydrogen applications, which will be performed by finite element method (software Ansys) and
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combined approach using numerical modeling and environmental metrology Key words: simulation, modeling, partial differential equations, hydraulics, inverse problem, sediment transport, peri-urban catchment
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environmental geophysics. This PhD project aims to advance the process-based understanding of SSF by combining state-of-the-art geophysical methods with controlled field experiments and numerical modeling
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for magnetic particle transport in fluid flows and validating the results through a laboratory-scale reactor. It combines numerical modeling, AI methods, and hands-on experimental work in the context of advanced
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synaptic resilience and the reliability of synaptic responses. The work primarily involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation
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, Infected macrophage populations. Perform parameter estimation using optimization and machine learning approaches Develop numerical schemes for high-dimensional structured PDEs (pseudospectral methods
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porous media. Proficiency in scientific programming (Python, C++, or Fortran) is essential. Demonstrated strong analytical and problem-solving skills, with familiarity in numerical methods and an interest