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GoncaGomes/README.md

Gonçalo Gomes

Applied AI researcher at Instituto de Telecomunicações and PhD candidate, focused on AI engineering and agentic systems. My background is in Electronics and Telecommunications Engineering, deep learning and signal processing.

I develop Python systems that connect LLMs and multimodal models with external tools, scientific documents and engineering software. My current work centres on agent workflows, Model Context Protocol (MCP) integrations and retrieval-augmented generation (RAG), with an emphasis on evaluation, traceable execution and reproducible results.

Representative Projects

Project Focus
cst-agentic A prototype using the OpenAI Agents SDK to explore progressive MCP tool discovery and controlled execution for CST automation.
cst-mcp Extending and validating an existing MCP server for CST Studio Suite, exposing tools for parametric modelling, simulation setup and result access.
Antenna Paper Extraction Agent-based extraction of antenna architecture from scientific papers, producing evidence reports and execution traces. The experimental MCP workflow is available on the development branch.
MCP PDF Ingestion A PDF evidence server adapted from an existing reviewer project, exposing deterministic retrieval tools and on-demand visual inspection through MCP.
Drone RF Classifier A compact PyTorch CNN for raw-IQ signal classification, with recording-disjoint splits, a synthetic demo and documented experiment provenance.

Ongoing Side Projects

Project Current focus
SportEvidence Developing a sports-science research assistant with RAG. Current work covers PDF ingestion, chunking, embeddings and vector storage; retrieval and source-grounded answer generation are next.
RepoLens An early-stage Python tool for Git repository analysis. Current work focuses on commit history retrieval, changed-file extraction and data modelling.

Technical Focus

  • Agentic AI: LLM tool use, OpenAI Agents SDK, agent workflows, progressive tool discovery and MCP.
  • LLM applications: RAG, multimodal models and scientific document processing.
  • AI engineering: Python, API integration, testing, command-line tools and Git.
  • Machine learning: PyTorch, TensorFlow, model evaluation and reproducible experiments.
  • Domain expertise: RF sensing, signal processing and electromagnetic simulation workflows.

Popular repositories Loading

  1. antenna_automation antenna_automation Public archive

    An automated antenna design project. Uses LLMs to extract antenna architecture and metrics to implement it in CST.

    Python

  2. antennaAuto antennaAuto Public archive

    An automated antenna design project. Uses LLMs to extract antenna architecture and metrics to implement it in CST.

    Python

  3. simpleChatIT simpleChatIT Public

    Python

  4. antenna_extraction antenna_extraction Public archive

    Python

  5. antenna-paper-extraction antenna-paper-extraction Public

    Evidence-grounded extraction of antenna architecture and reported results from scientific papers.

    Python

  6. drone-rf-classifier drone-rf-classifier Public

    Compact PyTorch CNN for raw-IQ drone RF classification, with recording-disjoint training, SigMF ingestion, synthetic demo and documented experiment provenance.

    Python