Defence Projects I’m Interested In (and How They Help Me Learn AI)

I’ve always been fascinated by the high-stakes world of defence tech. Radar, drones, secure comms—the whole lot feels like James-Bond gear minus the tuxedo budget. As a junior developer my honest question is, “What portfolio pieces would make a defence recruiter raise an eyebrow—in the good way—while still teaching me the AI skills I want?” Below is the short list I’m sketching for myself, plus why each project feeds both goals.

1. Tiny-Town Sensor Fusion Simulator

Why defence folks care: Modern aircraft and autonomous ground vehicles rely on fusing radar, LiDAR and camera feeds into one coherent picture.

What I can build: A Python playground that smashes together synthetic LiDAR point clouds and RGB images, then uses a PyTorch model to guess object positions. No real radar required—just open datasets and a sprinkle of Gaussian noise to mimic hardware quirks.

Skills unlocked:

• Data loaders in PyTorch for multi-modal inputs.
• Coordinate-frame math (rotation matrices, hooray!).
• Simple Kalman Filter for tracking—still shows up on job descriptions.

2. Real-Time Drone Telemetry Dashboard

Why defence folks care: Every drone in a test range needs a live command-and-control interface.

What I can build: A React + WebSocket app that plots UAV GPS points on a Leaflet map. I’ll simulate drone packets in a FastAPI backend, then later swap in a real telemetry feed if I buy a hobby drone.

Skills unlocked:

• WebSockets and back-pressure handling.
• Front-end state tricks with React hooks.
• Intro to geospatial formats (GeoJSON, CRS).

3. Classified-Style Chatbot (on Public Data)

Why defence folks care: Secure, on-prem LLM assistants for analysts are a hot R&D topic.

What I can build: A Retrieval-Augmented Generation chatbot that answers questions about open-source military specs. I’ll store vectors in a local Pinecone index and hit a local Llama-3 model using PyTorch or vLLM.

Skills unlocked:

• Prompt engineering and RAG pipelines.
• Fine-tuning or LoRA with PyTorch Lightning.
• Security best-practices: no outbound API calls, controllable context window.

4. “No-GPS” Navigation with Dead-Reckoning

Why defence folks care: GPS signals can be jammed; dead-reckoning becomes Plan-B.

What I can build: A simulation that ingests IMU data (accelerometer + gyroscope) from my phone and estimates position over time. PyTorch handles an LSTM that corrects drift using occasional “ground-truth” fixes.

Skills unlocked:

• Time-series preprocessing.
• Recurrent networks versus Transformers on small data.
• Error metrics like CEP (circular error probable).

5. Battle-Rhythm Anomaly Detector

Why defence folks care: Network-security teams watch for weird patterns that hint at intrusions.

What I can build: Generate fake log traffic, then train an auto-encoder in PyTorch to flag anomalies. Wrap it in a ShadCN-styled React dashboard that streams alerts.

Skills unlocked:

• Unsupervised learning.
• Streaming inference on CPU (cheap deploy).
• Clean separation between ML microservice and UI.

How I’ll tackle these projects without drowning

1. Month-long sprints—one project at a time to keep scope realistic.
2. Public GitHub repo per project with a README that explains the defence relevance in plain terms.
3. Blog each milestone (just like this post) so future employers see the learning curve, not just the finished code.
4. Seek mentoring on Discord or Reddit r/MachineLearning; constructive critique beats silent coding.
5. Security sanity-check—never include real restricted data, only open sources.

Final thoughts

None of these mini-projects will single-handedly land me a security clearance, but each is a talking point that shows two things: I understand a slice of defence-domain problems, and I can wield modern AI tools like PyTorch and LLMs to prototype solutions. If you’re also eyeing defence tech from the outside, steal these ideas, tweak them, and compare notes.