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, with proficiency in Python and deep learning frameworks like PyTorch, Hugging Face, sklearn, tensorflow. Excellent verbal and written communication skills Experience with GPU training and handling large
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frameworks like PyTorch, Hugging Face, sklearn, tensorflow. Excellent verbal and written communication skills Experience with GPU training and handling large medical datasets e.g., large magnetic resonance
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. Knowledge and Professional Experience: DFT-based methods. Scientific programming in Fortran, in MPI/OpenMP-parallelised codes. Knowledge of other languages (in particular python) and of GPU offloading will be
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such as NumPy, SciPy, PyTorch or TensorFlow Experience with C/C++ and GPU/accelerator platforms is an asset Hands‑on experience with software‑defined radio platforms, RF measurement equipment, or laboratory
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. Experience with high-speed data acquisition, signal processing, or FPGA/GPU-based DSP is considered an advantage. The ability to work independently while contributing effectively to a collaborative research
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provide competitive compensation packages and full support for conference travel and professional development. You'll have access to state-of-the-art high-performance computing infrastructure and GPU
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tracking), dataset curation, HPC/GPU programming, blockchain for secure data, C-family languages, and embodied AI/robotics are a plus. Experience with general network resilience, cellular automata
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. Experience with graph-based data analysis or anomaly detection methods. Exposure to high-performance or GPU-based computing environments. Demonstrated ability to contribute to publications or technical reports
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on retrospective Danish data. The research will include testing different levels of model scaling in terms of data amount and diversity, and training will take place both on a local GPU cluster and on the Gefion
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transformer architectures (e.g., ViT/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g