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Field
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inviting applications for a PhD Student (f/m/x) in the field of Theory and Methods for Non-equilibrium Theory and Atomistic Simulations of Complex Biomolecules Possible projects are variational free energy
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for the simulation of non-adiabatic exciton transfer dynamics in light-harvesting complexes. The research will use a combination of quantum and molecular dynamics simulations, electronic structure calculations, and
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of language, seeks to use bespoke probabilistic models and/or computational simulations to study possible routes of the evolution of human pragmatic practices. As a starting point, the project seeks to develop
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to challenging questions in the field of computational material design, especially with the help of CALPHAD-based methods. For further development of our simulation environment (https://github.com/cmatdesign
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Computational Biophysics/Chemistry (see also https://constructor.university/comp_phys ). The PhD position is focused on efficient algorithms for the simulation of non-adiabatic exciton transfer dynamics in light
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
DNA thermodynamic database, coarse-grained simulations of DNA motifs, and existing experimental data to establish an AI model that is able to guide the construction of desired secondary structures
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synthesis over all relevant length scales (e.g. cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) • High resolution analysis, monitoring of chemistry
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applied in Africa’s livestock sector. This work will involve modelling analyses using methodologies to be developed by the candidate in collaboration with their supervisors. This is one of two positions
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applications. Your main tasks: Develop and integrate degradation models for multiscale and multiphysics simulations of solid oxide cells Validate models using experimental data (e.g. IV curves, EIS measurements
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degradation models for multiscale and multiphysics simulations of solid oxide cells Validate models using experimental data (e.g. IV curves, EIS measurements) Apply ML methods in combination with CFD