Oge Marques

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Oge Marques

Oge Marques

@ProfessorOge

Sr. Strategic Researcher Engagement Manager at NVIDIA ◼︎Affiliate Professor at FAU ◼︎Book author & book series editor

Katılım Mayıs 2011
2.5K Takip Edilen1.3K Takipçiler
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NVIDIA AI Developer
NVIDIA AI Developer@NVIDIAAIDev·
NVIDIA’s Graduate Fellowship Program is now accepting applications for the 2026–2027 academic year. Selected Ph.D. students receive tuition and stipend coverage up to $60K, plus mentorship and technical support from top NVIDIA researchers during an NVIDIA internship. If you’re advancing work in AI, robotics, computer graphics, autonomous vehicles, healthcare, HPC, or related fields — this is your moment. 📅 Apply by Sept. 15, 2025: nvda.ws/3UsbEpe
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UCSF Imaging
UCSF Imaging@UCSFimaging·
We're hiring! We have openings in 9 subspecialties, including Neuroradiology, Breast Imaging, IR, Pediatrics & MSK. Join a leading academic department committed to clinical excellence, research & education. radiology.ucsf.edu/academic-affai…
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NVIDIA AI Developer
NVIDIA AI Developer@NVIDIAAIDev·
📣 Announced at #GTC25: NVIDIA cuML now accelerates scikit-learn, the most popular machine learning library, up to 50x with zero code changes. Zero code change acceleration is also supported for HDBSCAN and UMAP. Read the announcement blog: nvda.ws/4id2Nlu
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Oge Marques
Oge Marques@ProfessorOge·
You heard it at #SC24, NVIDIA #BioNeMo Framework is now open-source. Researchers worldwide can accelerate the development of life-saving treatments by tapping into the collection of accelerated programming tools for biomolecular research. #drugdiscovery bit.ly/48Z67wQ
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Oge Marques@ProfessorOge·
Hugging Face and NVIDIA are teaming up to advance robotics. By combining Hugging Face's LeRobot with NVIDIA AI, Omniverse, and #robotics technology, we're enabling researchers & developers to innovate. #CoRL2024 bit.ly/3UK7glT
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Oge Marques@ProfessorOge·
Learn more about the new NVIDIA AI Blueprint for video search and summarization, which speeds up the development of visual AI agents. It uses #VLM, LLMs, and the latest RAG techniques for long-form video understanding. bit.ly/3YW72L6
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Oge Marques@ProfessorOge·
Congratulations to all NVIDIA partners and Inception members featured in Time Magazine's Best Inventions of 2024. bit.ly/3YAlk2n
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Oge Marques@ProfessorOge·
Discover how to build powerful multimodal visual AI agents with NVIDIA NIM. Get hands-on with Jupyter notebooks for applications like streaming video alerts, structured text extraction, multimodal search, and few-shot classification. bit.ly/3CeWP3p
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Oge Marques@ProfessorOge·
Experience high-efficiency NVIDIA Llama-3.1-Nemotron-51B - a NAS-optimized model achieving 2x throughput while preserving accuracy runs on a single H100 GPU. Technical deep dive ➡️ bit.ly/4gE6ekA
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Oge Marques@ProfessorOge·
Try Hive’s AI-Generated Image Classification model, the latest powerful image classification tool for identifying and managing AI-generated content, on ai.nvidia.com today. bit.ly/3zD7jII
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Oge Marques@ProfessorOge·
Leading U.S. medical centers and research institutes are tapping into federated learning to detect cancer. This research collaboration uses NVIDIA FLARE and MONAI for secure, flexible AI model training for tumor segmentation. #NVIDIAAcademicGrant bit.ly/4exjZQl
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Oge Marques@ProfessorOge·
Generate code with Abacus AI’s Dracarys Large Language Model Dracarys, fine-tuned from Llama 3.1 70B and available from NVIDIA NIM microservice, supports a variety of applications, including data analysis, text summarization, and multi-language support. bit.ly/4e7eLLo
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Oge Marques@ProfessorOge·
📚 Introducing the new Generative AI Teaching Kit for educators developed w/ Professor Sam Raymond from @dartmouth. Enable students to explore the field of #GenAI w/ hands-on experimentation in #LLMs, diffusion models, & more. bit.ly/4e7Ae6o
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Pranav Rajpurkar
Pranav Rajpurkar@pranavrajpurkar·
💫Excited to share our new study: “Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs”. We’ve developed a system ReXKG to extract structured information from radiology reports, building a comprehensive knowledge graph for in-depth model analysis. Our study compares AI-generated vs. human-written radiology reports, evaluating both specialist and generalist models.
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