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-performance computing systems, GPU acceleration, and parallel file systems - Ability to communicate fluently in English, both spoken and written Additional qualifications - Knowledge of or interest in
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA INDUSTRIALE | Italy | about 1 month ago
Description Analysis and development of methodologies to accelerate the computation of numerical optimization through parallelization and the use of GPUs. Where to apply Website http://www.unibo.it Requirements
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transferability. Data pipelines for structured and unstructured data (images, text, social media, retail data) will be designed and validated on real-world case studies. Where to apply Website https
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Job Code 0005 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The successful applicant will assist in the adaptation of the PPMstar code to run well on GPU-accelerated
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, Cloud Service Deployment). Desired: Experience with High-Performance Computing or GPU programming (CUDA). Specialized knowledge of Neural Rendering (NeRF/3DGS) or Satellite Photogrammetry. Demonstrated
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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 (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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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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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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specialists and set clear priorities to realize projects effectively and on time, building infrastructure that makes complex analyses faster and more efficient. Your team optimizes virtual computing power (GPUs