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CourseAerospace, UAM, Drones & Space Applications open

AI-powered traffic control course at UAM

Online course of 300 hours in AI-based traffic control in UAM: 6 modules covering artificial intelligence for air traffic, trajectory and demand prediction, AI conflict resolution, flow and capacity optimisation, and AI assurance and regulation, with an assigned technical tutor.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Virtual lab · try it before you enrol

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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.

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Learning outcomes

What you will be able to do

01

Understand the AI techniques applicable to air traffic management.

02

Develop trajectory and demand prediction models.

03

Train reinforcement learning agents to resolve conflicts.

04

Optimise flows and sequences at vertiports.

05

Assess the explainability and robustness of models.

06

Apply the European regulatory framework for AI.

07

Design an AI solution for UAM traffic control.

Profile

Who it's for

  • Artificial intelligence engineers interested in aviation applications.
  • Aviation data scientists working with trajectory data.
  • UTM researchers.
  • ATM systems engineers adding machine learning to their toolset.
  • Optimisation algorithm developers.
  • AI assurance analysts.
  • Engineering or science graduates seeking to enter air navigation.
Standards & tools

What you'll work with

  • EASA Concept Paper Guidance for Level 1&2 Machine Learning Applications (guidance on machine learning in aviation)
  • Regulation (EU) 2024/1689 (Artificial Intelligence Act)
  • ISO/IEC 42001 (AI management systems)
  • ISO/IEC 23894 (AI risk management)
  • Implementing Regulation (EU) 2021/664 (regulatory framework for U-space)
  • ASTM F3548 (UAS traffic management USS interoperability)
  • Python
  • PyTorch
  • Ray RLlib
  • BlueSky ATM Simulator
  • Gymnasium
Syllabus

6 modules

  1. 1.1Traffic management problems that AI can address
  2. 1.2Supervised and unsupervised learning
  3. 1.3Reinforcement learning
  4. 1.4Deep neural networks
  5. 1.5Optimisation and metaheuristics
  6. 1.6Traffic and trajectory data
  7. 1.7State of the art in UTM
  8. 1.8Limitations of AI in critical systems
  9. 1.9ADS-B and Remote ID data sources
  10. 1.10Practical exercise: preparation of a trajectory dataset
  1. 2.1Trajectory prediction models
  2. 2.2LSTM networks and transformers
  3. 2.3Flight demand forecasting
  4. 2.4Handling meteorological uncertainty
  5. 2.5Estimation of arrival times
  6. 2.6Anomaly detection in trajectories
  7. 2.7Prediction metrics
  8. 2.8Time-series cross-validation
  9. 2.9Traffic feature engineering
  10. 2.10Lab: trajectory prediction with PyTorch
  1. 3.1Formulation as a Markov decision process
  2. 3.2Multi-agent reinforcement learning
  3. 3.3Training environments with BlueSky
  4. 3.4Reward functions
  5. 3.5Conflict resolution policies
  6. 3.6Comparison with geometric algorithms
  7. 3.7Robustness against disturbances
  8. 3.8Scalability with traffic density
  9. 3.9Generalisation to unseen scenarios
  10. 3.10Workshop: conflict resolution agent with RLlib
  1. 4.1Sequencing at vertiports
  2. 4.2Route and slot allocation
  3. 4.3Mixed-integer programming
  4. 4.4Genetic algorithms for allocation
  5. 4.5Balancing efficiency and fairness
  6. 4.6Dynamic airspace management
  7. 4.7Operator decision support
  8. 4.8Hybrid human-AI systems
  9. 4.9Recommender systems for controllers
  10. 4.10Case study: arrival sequencing at a vertihub
  1. 5.1EASA guidance for machine learning
  2. 5.2EASA AI roadmap
  3. 5.3European Artificial Intelligence Act
  4. 5.4Model explainability
  5. 5.5Verification of neural networks
  6. 5.6Data lifecycle management
  7. 5.7Human oversight of automated systems
  8. 5.8Bias and fairness
  9. 5.9Model validation in simulation
  10. 5.10Practical exercise: explainability assessment with SHAP
  1. 6.1Problem definition
  2. 6.2Dataset and environment
  3. 6.3Model design
  4. 6.4Training and tuning
  5. 6.5Evaluation against a baseline
  6. 6.6Assurance analysis
  7. 6.7Proposal for operational integration
  8. 6.8Model explainability analysis
  9. 6.9Report and presentation
  10. 6.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine the programme with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python, PyTorch, Ray RLlib, BlueSky ATM Simulator and Gymnasium, in free, academic or evaluation versions. Exercises are based on real procedures for training conflict resolution agents, trajectory prediction with deep learning and arrival sequence optimisation. Assessment is continuous by module plus the final project.

Standards covered: EASA Concept Paper Guidance for Level 1&2 Machine Learning Applications, Regulation (EU) 2024/1689, ISO/IEC 42001, ISO/IEC 23894, Implementing Regulation (EU) 2021/664, ASTM F3548.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of EASA machine learning guidance, Regulation (EU) 2024/1689 and ISO/IEC 42001 applied to real cases, not as a theoretical annex.
Industry software:Python, PyTorch, Ray RLlib, BlueSky ATM Simulator and Gymnasium, in free, academic or evaluation versions.
Final project:an AI solution for a UAM traffic control problem, trained, evaluated against a baseline and backed by an assurance and explainability analysis, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Define the problem
Frame the UAM traffic problem and prepare the dataset and environment.
Design the model
Choose and design the AI model for the problem.
Train and evaluate
Train and tune the model and evaluate it against a baseline.
Analyse assurance
Carry out the assurance and explainability analysis and the integration proposal.
Final project
Present the solution in a documented report, reviewed by your tutor.
Career opportunities

Where this takes you

Artificial intelligence engineerAviation data scientistUTM researcherATM systems engineerOptimisation algorithm developerAI assurance analyst
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • Internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python, PyTorch, Ray RLlib, BlueSky ATM Simulator and Gymnasium, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

No prior qualification is required; you need a computer able to run the programme's software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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