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Field
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, enhanced sampling, QM/MM) Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation
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frameworks (e.g., PyTorch). Familiarity with GPU-accelerated environments, virtualization tools, and prototyping using real testbeds (e.g., SDR). We expect a diploma in computer science or telecommunication
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authorship in papers in high-impact journals (IF>6) Experience with development of the PtyPy software Good understanding of Fourier optics GPU computing experience A background in Multibeam Ptychography is
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frameworks). Experience using open-source model ecosystems such as Hugging Face (Transformers, Datasets, Accelerate). Experience using or supporting supercomputing or GPU-enabled clusters. Experience with data
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frameworks (PyTorch, TensorFlow). Experience with dataset curation, annotation workflows, FAISS/embedding retrieval, LLM-based parsing, RAG-style pipeline, and GPU/HPC training. Familiarity with 3D data
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for Computer Graphics and Real-Time Rendering. By using ANNs, coded for high-performance on cross-vendor GPUs, we aim to create new techniques for global illumination and material models. The subject works with
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infrastructure, model training, and inference systems. You'll design, develop, and optimize scalable data pipelines and build multi-node GPU training and inference pipelines for foundational models. You'll also
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of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes
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algorithms as well as deep learning workflows on GPU servers (use of Git, Docker, and PyTorch) Design, implementation, and evaluation of spatial proteomics and multiplex analyses for characterizing the tumor
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. Preferred Qualifications : * Experience in programming on modern computing platforms including x86 and ARM-based CPUs and accelerators such as NVIDIA or AMD GPUs. * Experience working in an academic research