We Are Looking For Teammates To Bring Our Project To Life
Be part of the journey as we leave our mark on aviation history!
Artifical Intelligence Engineer
We can only accept final-year undergraduate, Master's, and PhD students.
We are looking for a team member who will develop learning models that process aviation data and contribute to that decision support engine.
- Ability to develop models using libraries such as Python and TensorFlow/Keras
- Understanding LSTM architecture, input shape logic, and the structure of time series models
- Familiarity with concepts such as model training cycle, epoch, batch size, callback
- Understanding the logic of creating model inputs by combining static and dynamic variables
- Understanding the inference pipeline logic to run the trained model on the backend
- Information on basic model optimization techniques (dropout, regularization)
- Ability to test model performance, speed, and latency metrics
Data Engineer
We can only accept final-year undergraduate, Master's, and PhD students.
We are looking for an infrastructure developer who will process real-time air traffic & meteorological data and provide quality data to the Al team.
- Proficiency in relational databases such as PostgresQL
- Database schema creation and table design skills
- Pulling data from the APl, parsing JSON, and processing data using ETL logic
- Writing scripts and designing data pipelines with Python
- Familiarity with managing time series data streams and sequence creation processes
- Understanding the logic of managing data flow between the backend and the database
- Performance awareness (appropriate JOIN structures, fast data preparation, and efficient storage)
Data Scientist
We can only accept final-year undergraduate, Master's, and PhD students.
We are seeking researchers to create datasets fed into the model by analyzing aviation and flight traffic data.
- Data cleaning, missing value management, and basic normalization knowledge
- Ability to manipulate data with Pandas / NumPy
- Understanding static-dynamic data structures and time series logic (sequence / sliding window)
- Basic level awareness of feature engineering
- Familiarity with rule based label generation and fundamental model evaluation metrics (loss, accuracy, ROC)