MasterFaculty of Engineering & Technology

AI-Powered Advanced System Engineering

Welcome to the advanced study of engineering! I am Prof. Dr. Emily MacDonald. As a professor and a pioneering force in the field of AI-Powered Advanced System Engineering, I bring a unique blend of engineering expertise and AI insight to the study of complex systems. I am honored to lead the AI-Powered Advanced System Engineering (M.Sc.) program at Nexier University. My motto is: "Engineering Intelligence for a Smarter Future".

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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 application of AI to the lifecycle of complex systems, learning to use AI for requirements analysis, system design, verification, and maintenance.

Practical focus

Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, leadership in technical project management.

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 engineering firms
Roles as AI engineers or systems architects
Consultancy in advanced AI-powered system engineering
Support roles in academic research projects on AI-powered system engineering

Career opportunities

Lead AI/Systems Integrator for aerospace, defense, or automotive companies
AI Engineer specializing in complex system lifecycle management
Systems Architect for large-scale industrial projects
Researcher in AI-Powered Advanced System Engineering

Jobs and projects

Cultivating an interdisciplinary approach, integrating systems engineering, AI, and various engineering domains
Developing strategic thinking for AI-driven system design and lifecycle management
Enhancing problem-solving through the analysis of complex engineering challenges using AI
Critical thinking for a comprehensive and nuanced understanding of AI-powered advanced system 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 Advanced systems thinking and AI and machine learning.
- Gaining expertise in modeling and simulation and requirements engineering.
- Developing problem-solving abilities for complex Leadership in technical project management.
- Cultivating an interdisciplinary approach, integrating systems engineering, AI, and various engineering domains at an advanced level.
Skills you build
- Mastering AI-powered techniques for predictive lifecycle optimization.
- Applying advanced AI to the lifecycle of complex systems, including requirements analysis, system design, verification, and maintenance.
- Interpreting and analyzing complex engineering systems and their implications for AI integration.
- Identifying potential failures and optimizing long-term performance and cost efficiency.

Listed courses

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

AI-Powered Advanced System Engineering

01AI for Requirements Engineering
1. FoundationsFoundations of AI for Requirements Engineering
  The learner can master advanced practical skills in Advanced systems thinking and AI and machine learning, as applied to AI for Requirements Engineering.
  The learner can gain expertise in modeling and simulation and requirements engineering, as applied to AI for Requirements Engineering.
2. MethodsMethods in AI for Requirements Engineering
  The learner can develop problem-solving abilities for complex Leadership in technical project management, as applied to AI for Requirements Engineering.
  The learner can cultivating an interdisciplinary approach, integrating systems engineering, AI, and various engineering domains at an advanced level, as applied to AI for Requirements Engineering.
3. ApplicationApplication of AI for Requirements Engineering
  The learner can master AI-powered techniques for predictive lifecycle optimization, as applied to AI for Requirements Engineering.
  The learner can apply advanced AI to the lifecycle of complex systems, including requirements analysis, system design, verification, and maintenance, as applied to AI for Requirements Engineering.
02AI-Driven System Design and Optimization
1. FoundationsFoundations of AI-Driven System Design and Optimization
  The learner can interpreting and analyze complex engineering systems and their implications for AI integration, as applied to AI-Driven System Design and Optimization.
  The learner can identify potential failures and optimizing long-term performance and cost efficiency, as applied to AI-Driven System Design and Optimization.
2. MethodsMethods in AI-Driven System Design and Optimization
  The learner can apply a method from AI-Driven System Design and Optimization to a documented case.
  The learner can select an appropriate method from AI-Driven System Design and Optimization for a stated problem.
3. ApplicationApplication of AI-Driven System Design and Optimization
  The learner can evaluate a practice of AI-Driven System Design and Optimization against a stated criterion.
  The learner can transfer AI-Driven System Design and Optimization to a new documented context.
03AI in System Verification and Validation
1. FoundationsFoundations of AI in System Verification and Validation
  The learner can explain the core terms of AI in System Verification and Validation.
  The learner can distinguish related ideas inside AI in System Verification and Validation.
2. MethodsMethods in AI in System Verification and Validation
  The learner can apply a method from AI in System Verification and Validation to a documented case.
  The learner can select an appropriate method from AI in System Verification and Validation for a stated problem.
3. ApplicationApplication of AI in System Verification and Validation
  The learner can evaluate a practice of AI in System Verification and Validation against a stated criterion.
  The learner can transfer AI in System Verification and Validation to a new documented context.
04AI-Powered Predictive Maintenance
1. FoundationsFoundations of AI-Powered Predictive Maintenance
  The learner can explain the core terms of AI-Powered Predictive Maintenance.
  The learner can distinguish related ideas inside AI-Powered Predictive Maintenance.
2. MethodsMethods in AI-Powered Predictive Maintenance
  The learner can apply a method from AI-Powered Predictive Maintenance to a documented case.
  The learner can select an appropriate method from AI-Powered Predictive Maintenance for a stated problem.
3. ApplicationApplication of AI-Powered Predictive Maintenance
  The learner can evaluate a practice of AI-Powered Predictive Maintenance against a stated criterion.
  The learner can transfer AI-Powered Predictive Maintenance to a new documented context.
05Advanced System Lifecycle Management with AI
1. FoundationsFoundations of Advanced System Lifecycle Management with AI
  The learner can explain the core terms of Advanced System Lifecycle Management with AI.
  The learner can distinguish related ideas inside Advanced System Lifecycle Management with AI.
2. MethodsMethods in Advanced System Lifecycle Management with AI
  The learner can apply a method from Advanced System Lifecycle Management with AI to a documented case.
  The learner can select an appropriate method from Advanced System Lifecycle Management with AI for a stated problem.
3. ApplicationApplication of Advanced System Lifecycle Management with AI
  The learner can evaluate a practice of Advanced System Lifecycle Management with AI against a stated criterion.
  The learner can transfer Advanced System Lifecycle Management with AI to a new documented context.
06Advanced Systems Thinking and AI
1. FoundationsFoundations of Advanced Systems Thinking and AI
  The learner can explain the core terms of Advanced Systems Thinking and AI.
  The learner can distinguish related ideas inside Advanced Systems Thinking and AI.
2. MethodsMethods in Advanced Systems Thinking and AI
  The learner can apply a method from Advanced Systems Thinking and AI to a documented case.
  The learner can select an appropriate method from Advanced Systems Thinking and AI for a stated problem.
3. ApplicationApplication of Advanced Systems Thinking and AI
  The learner can evaluate a practice of Advanced Systems Thinking and AI against a stated criterion.
  The learner can transfer Advanced Systems Thinking and AI to a new documented context.
07AI in System Modeling and Simulation
1. FoundationsFoundations of AI in System Modeling and Simulation
  The learner can explain the core terms of AI in System Modeling and Simulation.
  The learner can distinguish related ideas inside AI in System Modeling and Simulation.
2. MethodsMethods in AI in System Modeling and Simulation
  The learner can apply a method from AI in System Modeling and Simulation to a documented case.
  The learner can select an appropriate method from AI in System Modeling and Simulation for a stated problem.
3. ApplicationApplication of AI in System Modeling and Simulation
  The learner can evaluate a practice of AI in System Modeling and Simulation against a stated criterion.
  The learner can transfer AI in System Modeling and Simulation to a new documented context.
08Requirements Engineering for AI-Driven Systems
1. FoundationsFoundations of Requirements Engineering for AI-Driven Systems
  The learner can explain the core terms of Requirements Engineering for AI-Driven Systems.
  The learner can distinguish related ideas inside Requirements Engineering for AI-Driven Systems.
2. MethodsMethods in Requirements Engineering for AI-Driven Systems
  The learner can apply a method from Requirements Engineering for AI-Driven Systems to a documented case.
  The learner can select an appropriate method from Requirements Engineering for AI-Driven Systems for a stated problem.
3. ApplicationApplication of Requirements Engineering for AI-Driven Systems
  The learner can evaluate a practice of Requirements Engineering for AI-Driven Systems against a stated criterion.
  The learner can transfer Requirements Engineering for AI-Driven Systems to a new documented context.
09Case Studies in AI-Powered Advanced System Engineering
1. FoundationsFoundations of Case Studies in AI-Powered Advanced System Engineering
  The learner can explain the core terms of Case Studies in AI-Powered Advanced System Engineering.
  The learner can distinguish related ideas inside Case Studies in AI-Powered Advanced System Engineering.
2. MethodsMethods in Case Studies in AI-Powered Advanced System Engineering
  The learner can apply a method from Case Studies in AI-Powered Advanced System Engineering to a documented case.
  The learner can select an appropriate method from Case Studies in AI-Powered Advanced System Engineering for a stated problem.
3. ApplicationApplication of Case Studies in AI-Powered Advanced System Engineering
  The learner can evaluate a practice of Case Studies in AI-Powered Advanced System Engineering against a stated criterion.
  The learner can transfer Case Studies in AI-Powered Advanced System 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 application of AI to the lifecycle of complex systems, learning to use AI for requirements analysis, system design, verification, and maintenance. My work seamlessly integrates traditional engineering disciplines with cutting-edge artificial intelligence. I am widely recognized for my contributions, with publications like "AI for Automated Requirements Elicitation in Complex Systems" and "Generative Design Optimization for Engineering Systems" listed on these platforms. I hold prestigious memberships as a "Lead AI/Systems Integrator" at Lockheed Martin (or a equivalent) and a "Keynote Speaker" at the AIAA SciTech Forum (American Institute of Aeronautics and Astronautics). My thought leadership is evident through my advanced research on model-based systems engineering with AI, autonomous verification and validation, and the future of AI-driven engineering lifecycles, frequently featured in publications like Journal of Systems Engineering or IEEE Transactions on Systems, Man, and Cybernetics.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of AI-powered system engineering, focusing on Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, and Leadership in technical project management. 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 AI-powered advanced system engineering:

Blog Post (Current Academic Topic): "Model-Based Systems Engineering (MBSE) with AI: The Future of Complex Design." This blog post academically explores the integration of Artificial Intelligence into Model-Based Systems Engineering (MBSE). It discusses how AI enhances MBSE by automating tasks like requirements analysis, design synthesis, and verification, leading to more efficient, consistent, and robust development cycles for complex systems in aerospace, defense, and automotive industries.

Blog Post (Controversial Topic): "The Algorithmic Engineer: When AI Designs and Optimizes Our World – Efficiency or Unforeseen Consequences? The Ethical Crossroads of Autonomous Systems." This article provocatively discusses the highly controversial future where advanced AI systems autonomously design, manage, and optimize complex engineering systems, from smart cities and energy grids to autonomous vehicles and critical infrastructure, with minimal human oversight. It questions whether AI, despite its potential for hyper-efficiency and groundbreaking innovation, could inadvertently lead to unpredictable systemic failures, "black box" design flaws, or decisions that prioritize algorithmic efficiency over human values or safety. It raises profound ethical questions about accountability in AI-designed infrastructure, the potential for unintended societal impacts, and the imperative to ensure human control over the engineered world.

Article: "AI for Automated System Verification and Validation." This article details the application of AI algorithms for automating system verification and validation throughout the engineering lifecycle. It explores how machine learning can analyze design specifications, test results, and operational data to identify discrepancies, predict compliance issues, and ensure systems meet their intended requirements, accelerating the V&V process.

Peer-Reviewed Journal Article: "AI-Powered Generative Design for Complex Engineering Systems." Published in the International Journal of Advanced Engineering Design, this article presents groundbreaking research on mastering the application of AI to the lifecycle of complex systems. It details novel approaches to using AI for requirements analysis, system design, and maintenance, showcasing generative design techniques for optimized engineering solutions.

Book: "The AI Systems Lifecycle: From Concept to Optimization." This book provides advanced insights into mastering the application of AI to the lifecycle of complex systems. It covers using AI for requirements analysis, system design, verification, and maintenance.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of AI-powered system engineering:

"Requirements Engineering for AI-Driven Systems" (Technical Manual).

"AI in System Modeling and Simulation: Predicting Complex Behavior" (Research Paper).

"Leadership in AI Engineering Projects: Best Practices" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Predictive Lifecycle Optimizer. When a student designs a complex system, I can instantly use the GAF engine to simulate its entire lifecycle, from design to maintenance. This tool predicts potential failures, optimizes for long-term performance and cost efficiency, and highlights AI-driven interventions at each stage, ensuring a robust and maintainable system from inception.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Requirements Validation Synthesizer. When students are gathering requirements for an AI-powered system, I can instantly activate a GAF-powered "Requirements Validation Synthesizer." This tool analyzes the completeness, consistency, and feasibility of the requirements using AI, identifies ambiguities or contradictions, and generates a prioritized list of clarification questions, ensuring a robust foundation for system development.

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. Emily MacDonald

Mastering the application of AI to the lifecycle of complex systems, learning to use AI for requirements analysis, system design, verification, and maintenance.

Meet your professor Open the classroom

AI Super Mentor

Dr. Tiara Simatupang

Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, leadership in technical project management.

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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AI-Powered Advanced System Engineering

Academic focus

Practical focus

Internships in technology companies or engineering firms

Roles as AI engineers or systems architects

Consultancy in advanced AI-powered system engineering

Support roles in academic research projects on AI-powered system engineering

Lead AI/Systems Integrator for aerospace, defense, or automotive companies

AI Engineer specializing in complex system lifecycle management

Systems Architect for large-scale industrial projects

Researcher in AI-Powered Advanced System Engineering

Cultivating an interdisciplinary approach, integrating systems engineering, AI, and various engineering domains

Developing strategic thinking for AI-driven system design and lifecycle management

Enhancing problem-solving through the analysis of complex engineering challenges using AI

Critical thinking for a comprehensive and nuanced understanding of AI-powered advanced system engineering

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Emily MacDonald

Dr. Tiara Simatupang

Find the program that expands your universe.