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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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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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performance in hydrogenation, dehydrogenation, and hydrogen transfer reactions. Detailed characterization and kinetic studies will be performed to test computational predictions and microkinetic models, and to
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samples, including oocytes and early embryos. Perform in vitro chemical and analytical validation of metabolite detection methods (e.g. oligonucleotide synthesis, reaction optimization, and mass
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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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, SES, mental health, and educational performance, the project will be in the position to consider the entire country since 1940. A population-based cohort study comprising 100 000 genotyped families with
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., inversion products, mode constraints, forward modelling, or comparable observational–simulation comparison frameworks). Experience working with large HPC-produced datasets and/or high-performance computing
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biophysical assays (e.g., micropipette aspiration, atomic force microscopy), and help establish robust protocols for force mapping in early embryos. Perform spatially resolved proteomic imaging to link gene
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medication type, age at first use, and sociocultural and geographical context. As a PhD fellow, you will be working with large-scale longitudinal data, managing data, writing scripts, performing statistical