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programming skills (e.g., Fortran, Python; familiarity with Linux/HPC environments). strong motivation to work in an interdisciplinary environment bridging molecular theory, nanoscience, and light–matter
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learning and signal processing libraries; You have HPC/GPU computing experience, including running deep learning workloads on compute clusters (CUDA-compatible GPUs, multi-GPU training, Slurm). Your master's
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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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Knowledge of deep learning architectures, graph neural networks, or uncertainty quantification Familiarity with HPC environments Language Requirements: Applicants must demonstrate at least B2-level
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working language Training at a strong medical research university (MUI) with access to HPC, expert bioinformatics mentorship, and close experimental collaborations A project with clear scientific novelty
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HPC systems Practical experience working with conda environments and Python scripting Motivation to contribute to the development of an open-source molecular modeling platform for soil components Hands
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(MUI) with access to HPC, expert bioinformatics mentorship, and close experimental collaborations A project with clear scientific novelty, real translational relevance, and multiple publishable
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is 4 years. The PhD candidate will join COMPSOIL, a dynamic and international research environment, benefiting from the University's high-performance computing (HPC) infrastructure and opportunities
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publications at top-tier venues such as CVPR, ICCV, ECCV, NeurIPS or ICRA. You will have access to extensive compute resources at TU Delft, ranging from local GPU servers to large-scale HPC infrastructure