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and accelerate the development of more high-performing PNSEs. The ultimate goal of the project is to develop, implement, and validate novel deep-learning models for molecular dynamics and coarse-grained
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations Interest in close collaboration with experimental researchers
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position that will give promising researchers an opportunity for academic development through a PhD education leading to a doctoral degree. The hired candidate will be admitted to the PhD program in Science
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the Department). About the project/work tasks The research project will focus on developing quantum hyperheuristics, exploring how quantum computing can enhance hyperheuristic frameworks for solving large-scale
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computing on high-performance supercomputers such as Saga and LUMI to accelerate drug candidate identification. The position may include secondments to industry partners for biophysical analyses (X-ray
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Glide, AutoDOCK) Experience with molecular dynamics simulation packages (AMBER, NAMD, GROMACS) Familiarity with high-performance computing or GPU-accelerated simulations Interest in G protein-coupled
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statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations
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calibration Familiarity with high-performance computing or large-scale simulations Interest in close collaboration with experimental researchers Language requirement: Good oral and written communication skills
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
with high-performance computing or large-scale simulations Interest in close collaboration with experimental researchers Language requirement: Good oral and written communication skills in English