MasterFaculty of Engineering & Technology

Autonomous Urban Air Mobility Systems Engineering

Welcome to the advanced study of urban air mobility! I am Prof. Dr. Alice Santos. As a professor and a pioneering force in the field of Autonomous Urban Air Mobility Systems Engineering, I bring a unique blend of engineering expertise and AI insight to the study of aerial mobility. I am honored to lead the Autonomous Urban Air Mobility Systems Engineering (M.Sc.) program at Nexier University. My motto is: "Elevating Urban Life, One Flight at a Time".

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the systems engineering for autonomous air taxi networks. Specializes in eVTOL vehicle design, autonomous navigation and control, and air traffic management systems.

Practical focus

Aerospace engineering, autonomous systems, control theory, AI for navigation, safety-critical systems engineering, leadership in the aerospace industry.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or aerospace firms
Roles as UAM engineers or air traffic management specialists
Consultancy in advanced autonomous urban air mobility systems engineering
Support roles in academic research projects on autonomous UAM

Career opportunities

Director of Autonomous Aviation for aerospace companies or technology firms
Autonomous Systems Engineer for UAM platforms
Air Traffic Management Specialist for urban airspaces
Researcher in Autonomous Urban Air Mobility Systems Engineering

Jobs and projects

Cultivating an interdisciplinary approach, integrating aerospace engineering, computer science, and urban planning
Developing strategic thinking for autonomous urban air mobility solutions and air traffic management
Enhancing problem-solving through the analysis of complex aerospace challenges
Critical thinking for a comprehensive and nuanced understanding of autonomous urban air mobility systems engineering

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Aerospace engineering and autonomous systems.
- Gaining expertise in control theory and AI for navigation.
- Developing problem-solving abilities for complex safety-critical systems engineering.
- Cultivating an interdisciplinary approach, integrating aerospace engineering, computer science, and urban planning at an advanced level.
Skills you build
- Mastering AI-powered techniques for autonomous flight safety validation.
- Applying advanced systems engineering to autonomous air taxi networks.
- Interpreting and analyzing complex eVTOL vehicle designs and their implications for autonomous navigation and control.
- Identifying potential single points of failure and optimizing for maximum safety and resilience.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Autonomous Urban Air Mobility Systems Engineering

01eVTOL Vehicle Design and Aerodynamics
1. FoundationsFoundations of eVTOL Vehicle Design and Aerodynamics
  The learner can master advanced practical skills in Aerospace engineering and autonomous systems, as applied to eVTOL Vehicle Design and Aerodynamics.
  The learner can gain expertise in control theory and AI for navigation, as applied to eVTOL Vehicle Design and Aerodynamics.
2. MethodsMethods in eVTOL Vehicle Design and Aerodynamics
  The learner can develop problem-solving abilities for complex safety-critical systems engineering, as applied to eVTOL Vehicle Design and Aerodynamics.
  The learner can cultivating an interdisciplinary approach, integrating aerospace engineering, computer science, and urban planning at an advanced level, as applied to eVTOL Vehicle Design and Aerodynamics.
3. ApplicationApplication of eVTOL Vehicle Design and Aerodynamics
  The learner can master AI-powered techniques for autonomous flight safety validation, as applied to eVTOL Vehicle Design and Aerodynamics.
  The learner can apply advanced systems engineering to autonomous air taxi networks, as applied to eVTOL Vehicle Design and Aerodynamics.
02Autonomous Flight Control Systems
1. FoundationsFoundations of Autonomous Flight Control Systems
  The learner can interpreting and analyze complex eVTOL vehicle designs and their implications for autonomous navigation and control, as applied to Autonomous Flight Control Systems.
  The learner can identify potential single points of failure and optimizing for maximum safety and resilience, as applied to Autonomous Flight Control Systems.
2. MethodsMethods in Autonomous Flight Control Systems
  The learner can apply a method from Autonomous Flight Control Systems to a documented case.
  The learner can select an appropriate method from Autonomous Flight Control Systems for a stated problem.
3. ApplicationApplication of Autonomous Flight Control Systems
  The learner can evaluate a practice of Autonomous Flight Control Systems against a stated criterion.
  The learner can transfer Autonomous Flight Control Systems to a new documented context.
03AI for Urban Air Traffic Management
1. FoundationsFoundations of AI for Urban Air Traffic Management
  The learner can explain the core terms of AI for Urban Air Traffic Management.
  The learner can distinguish related ideas inside AI for Urban Air Traffic Management.
2. MethodsMethods in AI for Urban Air Traffic Management
  The learner can apply a method from AI for Urban Air Traffic Management to a documented case.
  The learner can select an appropriate method from AI for Urban Air Traffic Management for a stated problem.
3. ApplicationApplication of AI for Urban Air Traffic Management
  The learner can evaluate a practice of AI for Urban Air Traffic Management against a stated criterion.
  The learner can transfer AI for Urban Air Traffic Management to a new documented context.
04Safety-Critical Systems Engineering for UAM
1. FoundationsFoundations of Safety-Critical Systems Engineering for UAM
  The learner can explain the core terms of Safety-Critical Systems Engineering for UAM.
  The learner can distinguish related ideas inside Safety-Critical Systems Engineering for UAM.
2. MethodsMethods in Safety-Critical Systems Engineering for UAM
  The learner can apply a method from Safety-Critical Systems Engineering for UAM to a documented case.
  The learner can select an appropriate method from Safety-Critical Systems Engineering for UAM for a stated problem.
3. ApplicationApplication of Safety-Critical Systems Engineering for UAM
  The learner can evaluate a practice of Safety-Critical Systems Engineering for UAM against a stated criterion.
  The learner can transfer Safety-Critical Systems Engineering for UAM to a new documented context.
05Urban Airspace Integration and Regulations
1. FoundationsFoundations of Urban Airspace Integration and Regulations
  The learner can explain the core terms of Urban Airspace Integration and Regulations.
  The learner can distinguish related ideas inside Urban Airspace Integration and Regulations.
2. MethodsMethods in Urban Airspace Integration and Regulations
  The learner can apply a method from Urban Airspace Integration and Regulations to a documented case.
  The learner can select an appropriate method from Urban Airspace Integration and Regulations for a stated problem.
3. ApplicationApplication of Urban Airspace Integration and Regulations
  The learner can evaluate a practice of Urban Airspace Integration and Regulations against a stated criterion.
  The learner can transfer Urban Airspace Integration and Regulations to a new documented context.
06Advanced eVTOL Design and Control
1. FoundationsFoundations of Advanced eVTOL Design and Control
  The learner can explain the core terms of Advanced eVTOL Design and Control.
  The learner can distinguish related ideas inside Advanced eVTOL Design and Control.
2. MethodsMethods in Advanced eVTOL Design and Control
  The learner can apply a method from Advanced eVTOL Design and Control to a documented case.
  The learner can select an appropriate method from Advanced eVTOL Design and Control for a stated problem.
3. ApplicationApplication of Advanced eVTOL Design and Control
  The learner can evaluate a practice of Advanced eVTOL Design and Control against a stated criterion.
  The learner can transfer Advanced eVTOL Design and Control to a new documented context.
07AI for Autonomous Navigation and Obstacle Avoidance
1. FoundationsFoundations of AI for Autonomous Navigation and Obstacle Avoidance
  The learner can explain the core terms of AI for Autonomous Navigation and Obstacle Avoidance.
  The learner can distinguish related ideas inside AI for Autonomous Navigation and Obstacle Avoidance.
2. MethodsMethods in AI for Autonomous Navigation and Obstacle Avoidance
  The learner can apply a method from AI for Autonomous Navigation and Obstacle Avoidance to a documented case.
  The learner can select an appropriate method from AI for Autonomous Navigation and Obstacle Avoidance for a stated problem.
3. ApplicationApplication of AI for Autonomous Navigation and Obstacle Avoidance
  The learner can evaluate a practice of AI for Autonomous Navigation and Obstacle Avoidance against a stated criterion.
  The learner can transfer AI for Autonomous Navigation and Obstacle Avoidance to a new documented context.
08Case Studies in Autonomous Urban Air Mobility Systems Engineering
1. FoundationsFoundations of Case Studies in Autonomous Urban Air Mobility Systems Engineering
  The learner can explain the core terms of Case Studies in Autonomous Urban Air Mobility Systems Engineering.
  The learner can distinguish related ideas inside Case Studies in Autonomous Urban Air Mobility Systems Engineering.
2. MethodsMethods in Case Studies in Autonomous Urban Air Mobility Systems Engineering
  The learner can apply a method from Case Studies in Autonomous Urban Air Mobility Systems Engineering to a documented case.
  The learner can select an appropriate method from Case Studies in Autonomous Urban Air Mobility Systems Engineering for a stated problem.
3. ApplicationApplication of Case Studies in Autonomous Urban Air Mobility Systems Engineering
  The learner can evaluate a practice of Case Studies in Autonomous Urban Air Mobility Systems Engineering against a stated criterion.
  The learner can transfer Case Studies in Autonomous Urban Air Mobility Systems Engineering to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mastering the systems engineering for autonomous air taxi networks. I specialize in eVTOL vehicle design, autonomous navigation and control, and air traffic management systems. My work seamlessly integrates aerospace engineering, computer science, and urban planning. I am widely recognized for my contributions, with publications like "AI for Resilient Autonomous Flight Control in Dynamic Urban Environments" and "Decentralized Air Traffic Control for High-Density UAM Operations" listed on these platforms. I hold prestigious memberships as a "Director of Autonomous Aviation" at Embraer (or a equivalent) and a "Keynote Speaker" at the AIAA Aviation Forum. My thought leadership is evident through my advanced research on safety-critical autonomous systems, urban airspace integration, and the future of AI in aviation, frequently featured in publications like Journal of Guidance, Control, and Dynamics or Aerospace Science and Technology.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of urban air mobility, focusing on Aerospace engineering, autonomous systems, control theory, AI for navigation, safety-critical systems engineering, and Leadership in the aerospace industry. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on autonomous urban air mobility systems engineering:

Blog Post (Current Academic Topic): "AI for Urban Air Traffic Flow Optimization: The Future of Congestion-Free Skies." This blog post academically explores the pivotal role of Artificial Intelligence in optimizing urban air traffic flow for UAM. It discusses how AI algorithms can predict traffic patterns, dynamically route eVTOLs, and resolve conflicts in real-time, ensuring safe, efficient, and congestion-free operation of autonomous air taxi networks over dense cityscapes.

Blog Post (Controversial Topic): "The Sky's No Limit: When Autonomous Air Taxis Rule the City - Freedom or Surveillance? The Ethical Dilemma of Ubiquitous Aerial Mobility." This article provocatively discusses the highly controversial future where advanced autonomous air taxis become a dominant mode of urban transportation, navigating complex cityscapes with minimal human intervention. It questions whether this ubiquitous aerial mobility, despite its potential for unprecedented convenience and efficiency, could inadvertently lead to a loss of privacy due to constant aerial surveillance, increased noise pollution, or a widening socio-economic divide in access to rapid transport. It raises profound ethical questions about public airspace rights, data collection from aerial platforms, and the imperative to ensure equitable and sustainable urban development in the age of air taxis.

Article: "Autonomous Navigation and Obstacle Avoidance for Urban Air Vehicles." This article details advanced techniques for autonomous navigation and obstacle avoidance in urban air vehicles. It explores how sensor fusion (LiDAR, cameras, radar) combined with AI algorithms enables eVTOLs to perceive their surroundings, build accurate maps, and safely navigate complex urban canyons and dynamic airspaces.

Peer-Reviewed Journal Article: "AI-Driven Resilient Control Systems for Autonomous eVTOL Aircraft." Published in the International Journal of Autonomous Aviation, this article presents groundbreaking research on mastering the systems engineering for autonomous air taxi networks. It specializes in eVTOL vehicle design, autonomous navigation and control, and air traffic management systems, showcasing novel AI-driven approaches for robust and safe autonomous flight.

Book: "Autonomous Skies: Systems Engineering for Urban Air Mobility." This book provides advanced insights into mastering the systems engineering for autonomous air taxi networks. It covers eVTOL vehicle design, autonomous navigation and control, and air traffic management systems.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of urban air mobility:

"Advanced Control Systems for Multi-Rotor eVTOLs" (Technical Manual).

"AI for Real-time Obstacle Avoidance in Unstructured Environments" (Research Paper).

"Systems Integration for Urban Air Mobility Platforms" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Autonomous Flight Safety Validator. When a student designs an autonomous flight control system for an air taxi, I can instantly use the GAF engine to simulate its behavior under extreme fault conditions and unexpected environmental variables. This tool identifies potential single points of failure, predicts cascading system failures, and optimizes for maximum safety and resilience, ensuring the integrity of safety-critical systems.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Urban Airspace Simulator. When students are designing air traffic management systems, I can instantly activate a GAF-powered "Urban Airspace Simulator." This tool models dense urban environments, simulates hundreds of concurrent eVTOL flights, and identifies potential traffic conflicts, airspace violations, or routing inefficiencies, allowing for optimal design of future air taxi networks.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Alice Santos

Mastering the systems engineering for autonomous air taxi networks. Specializes in eVTOL vehicle design, autonomous navigation and control, and air traffic management systems.

Meet your professor Open the classroom

AI Super Mentor

Dr. Yuna Kikuchi

Aerospace engineering, autonomous systems, control theory, AI for navigation, safety-critical systems engineering, leadership in the aerospace industry.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Autonomous Urban Air Mobility Systems Engineering

Academic focus

Practical focus

Internships in technology companies or aerospace firms

Roles as UAM engineers or air traffic management specialists

Consultancy in advanced autonomous urban air mobility systems engineering

Support roles in academic research projects on autonomous UAM

Director of Autonomous Aviation for aerospace companies or technology firms

Autonomous Systems Engineer for UAM platforms

Air Traffic Management Specialist for urban airspaces

Researcher in Autonomous Urban Air Mobility Systems Engineering

Cultivating an interdisciplinary approach, integrating aerospace engineering, computer science, and urban planning

Developing strategic thinking for autonomous urban air mobility solutions and air traffic management

Enhancing problem-solving through the analysis of complex aerospace challenges

Critical thinking for a comprehensive and nuanced understanding of autonomous urban air mobility systems engineering

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Alice Santos

Dr. Yuna Kikuchi

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