Contact person: Business Client

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Location: beijing, Qatar

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"SysML diagram generation using prompt engineering and RAG (Retrieval-Augmented Generation) technology for flood rescue scenarios Drone Search and Rescue mission. requires significant improvements in innovation, technical depth, logical structure, and experimental validation.

Required Skills
Essential Technical Skills:

Advanced knowledge of SysML (Systems Modeling Language) and diagram generation
Expertise in RAG (Retrieval-Augmented Generation) architecture and implementation
Strong background in prompt engineering and LLM optimization
Experience with AI/ML model evaluation and experimental design
Proficiency in research methodology and technical writing

Domain Knowledge:

Understanding of systems engineering principles
Familiarity with disaster response systems or emergency management (flood rescue context)
Knowledge of software engineering methodologies

Preferred Qualifications:

Master's or PhD in Computer Science, Software Engineering, or related field
Published research in AI, NLP, or systems modeling
Experience with diagram generation or visual modeling systems


Scope of Work
1. Innovation Enhancement (Critical Priority)

Develop novel technical contributions: Propose and document new frameworks, algorithms, or improvement strategies beyond existing RAG and prompt engineering methods
Identify unique technical innovations: Design custom approaches that go beyond "off-the-shelf" closed-source model usage
Create a clear innovation statement: Articulate what makes this research contributory to the field
2. Domain Adaptation Justification (technical report)

Strengthen flood rescue scenario alignment: Provide detailed justification for why RAG and prompt engineering are specifically suited for flood rescue SysML generation
Document domain-specific customizations: Explain technical adaptations made for emergency response requirements
Demonstrate specialized features: Show how the approach addresses unique challenges in disaster response modeling

4. Methodology Expansion (Technical report)

Substantially expand technical content: Add detailed algorithm descriptions, architecture diagrams, and implementation specifics
Increase workload demonstration: Include more comprehensive technical discussions, ablation studies, or additional experiments
Provide rigorous technical depth: Move beyond surface-level descriptions to in-depth technical analysis

5. Experimental Section Restructuring (technical report)

Reorganize with clear logical flow: Structure experiments with clear hypotheses, methodologies, results, and analysis sections
Improve diagram quality: Either regenerate higher-quality SysML diagrams or provide better examples
Strengthen validation: Design experiments that clearly demonstrate the effectiveness of the proposed approach
Add quantitative metrics: Include measurable evaluation criteria beyond visual inspection

Deliverables

technical report with all tracked changes
Summary document outlining major changes
New/improved SysML diagrams (if applicable, in appropriate format)
Supplementary materials (code documentation, additional experimental results if needed)" (client-provided description)