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Community, Education, and Outreach
Scientific Python Community Building,
Python Usage in Education,
Diversity and Inclusion in Scientific Python,
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Computational Tools and Scientific Python Infrastructure
Core Scientific Python Libraries,
GPU Acceleration for Scientific Computing,
Distributed Computing & Parallel Processing,
Just-in-Time Compilation,
Performance Optimization and Profiling,
Mathematical and Statistical Computing,
Visualization and Data Analysis Tools,
Numerical Simulation and Modeling Frameworks,
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Interdisciplinary Frontiers and other Scientific Python Applications
Brain-Computer Interfaces,
Digital Humanities and Computational Social Sciences,
Reproducible Research and Scientific Workflows,
Smart Cities,
Bioinspired Technologies,
Space Exploration and Monitoring,
Computational Archaeology and Historical Reconstruction,
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Physical Sciences and Engineering
Quantum Computing and Quantum Sensors,
Quantum Machine Learning,
Physics Simulations,
Energy Research and Fusion Energy,
Astrophysics and Cosmology,
Gravitational Wave Astronomy,
Dark Matter and Dark Energy Studies,
Exoplanet Discovery and Characterization,
Materials Science,
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Life Sciences and Biomedicine
Genomic Technologies and Applications,
Computational Biology and Bioinformatics,
Protein Folding Prediction,
Personalized and Regenerative Medicine,
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Environmental and Earth Sciences
Climate Science,
Biodiversity and Conservation,
Ocean Acidification Studies,
Environmental Remediation,
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Large Language Models (LLMs), Neural Networks and AI Development
Neural Network Architecture Design and Optimization,
Quantization Methods,
Federated Learning,
Group Relative Policy Optimization Algorithms,
Long Chain-of-Thought Reasoning Research,
LLM Training, Fine-tuning and Inference Frameworks,
Self-evolution through Reinforcement Learning,
Explainable AI Systems,
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Applied AI & LLM Technologies and Use Cases
Ethical AI Frameworks and Guidelines,
Sustainable Computation for AI,
Benchmarking Frameworks and Methodologies,
Evaluation Metrics and Standards,
Datasets for LLM Training and Fine-tuning,
Hybrid System Architectures,
Retrieval-Augmented Generation System Design and Optimization,
AI Agents,
Domain-specific Model Adaptation,
Prompt Engineering and Interface Design,
Automatic Local Fine-tuning for RAG,
Tools Built on LLMs (Coding Assistants, Search),
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