Aviation Digital Technology Management MSc

As more intelligent and conscious aircraft enter service, aircraft and support systems are also becoming more intelligent. The future of aviation increasingly points towards electric and hydrogen-powered technologies, autonomous flying taxis and space commercialisation. These new realities will require leaders with digital skills to innovate solutions towards sustainability and efficiency in aviation.

The MSc in Aviation Digital Technology Management develops professionals with the ability to innovate and apply digital technology in the aerospace industry. The course gives graduates from aeronautical and engineering backgrounds the digital skills and capabilities required to develop a career that goes beyond design and manufacture, and will open up wider aviation industry opportunities.

Overview

Start dateOctober
DurationFull-time one year; part-time up to three years; PgCert 1 year; PgDip 2 years.
DeliveryTaught modules 40%, group project 20% (or dissertation for part-time students), individual project 40%.
QualificationMSc, PgDip, PgCert
麻豆传媒AV typeFull-time / Part-time
CampusCranfield campus

Who is it for?

This course is applicable to a broad range of applicants with backgrounds from aerospace, engineering, maths, physics and computing to experienced professionals looking to gain new skills in the area of digital aviation technology.

The course is also a route for non-aerospace engineering and computing graduates who aspire to enter the aviation industry. In addition, the course is a career development path for aerospace industry professionals to boost their digital and innovation skills.

Why this course?

New and emerging generations of connected aircraft are entering service at pace. Aviation digitalisation spans the aircraft, airline, airport, airspace, MRO, ground operations, lessors and the wider ecosystem. New skills in the Internet of Things, robotics, AI, machine learning, AR/VR, big data analytics, predictive maintenance, blockchain, etc. are sought by the industry. While the digital technology is similar to other industrial sectors, the safety conscious and highly-regulated aviation industry imposes technology adoption hurdles specific to the aerospace regulatory and culture.

During the course you will have access to:

The unique Cranfield global research airport learning environment.
World-leading research centres. 麻豆传媒AV IVHM Centre has been leading research in the predictive health maintenance with technology adopted by major aircraft manufacturers.
Digital MRO and hangar laboratories together with the Cranfield Boeing 737-400 ground demonstrator.
HILDA digital high performance computing ecosystem with up to 10.5 PetaFlops processing power, 2.5 PB storage and 200GBits/s network.
Industrial standard aviation IT systems.

You will gain hands-on experience via practical research projects that make up 60% of the study. The group design project (GDP) aims to develop your professional practice to work as part of a team, plan and manage projects, and communicate ideas and results. During GDP project you will design, implement, validate and test an aspect of digital aviation, applying the knowledge acquired in the taught modules and integrate the various methods learnt.

The individual research projects (IRPs) allow the students to specialise in a digital aviation topic relevant to their career development. The real-world relevance of the research project topics is an effective differentiator in the job market.

Informed by industry

The MSc in Aviation Digital Technology Management has been developed following consultation with our Industrial Advisory Board (IAB), comprising leaders from Boeing, Etihad, easyJet, Saab, STS Aviation, SITA, Thales, TUI, and others across the aviation sector. As such, the course content meets the requirements of the industry, now and in the future.

Course details

In this section

The MSc course consists of three weighted components, taught modules and individual research project, and a group project.

Between October and January, students study eight taught modules. The content delivery is organised as one week contact time followed by private study time to complete the assessments. Pre-work and post-work are incorporated as appropriate. Private study weeks are spaced through the October to January period.

The learning journey support students in two complementary digital careers directions: a technical direction to add cyber-physical capability to automate aircraft and facilities operations; and an informatics direction to focus on the management and exploitation of the large volume of aviation data. The taught modules build the components for progression to the projects. The Aviation Digitalisation module establishes the foundation of the aviation industrial ecosystem. The Digital Engineering and the Data-Centric Aircraft Systems modules together build the technical basis of intelligent aviation. The Predictive Maintenance Technology and the Aerospace Inspection and Monitoring Tools modules cover the technology for digitalisation of the aviation asset operations and maintenance. The Digital Aviation Operations and Maintenance Management and the Digital Aviation Supply Chain Management modules bring together the internal and external views of aviation management. The Communications and Cybersecurity in Aviation module covers the secure communication needed in all aspects of technology and operations. The pre-requisite module path is illustrated in the diagram.

digital aviation

Students are supported in their learning and personal development through participation in: industry seminars, group poster session, group discussions, group presentations, video demonstrations, case studies, laboratory experiments, coursework and project work. Students will receive hands-on experience accessing equipment and facilities within our and .

Course delivery

Taught modules 40%, group project 20% (or dissertation for part-time students), individual project 40%.

The group design project is a full time challenge to tackle a real industrial problem within a fixed 12 week timescale. The group of students are expected to professionally deliver working results to the sponsor at the end of the project. This learn-by-doing approach integrates the knowledge students gained in the taught modules. The collaborative experience prepares students to work in teams with members having diverse backgrounds and expertise, project management and technical presentations.

Example projects include:

Robotic inspection to support aircraft maintenance.
Fusion of location and camera sensing to support maintenance personnel electronic sign-offs.
Autonomous collision avoidance in aircraft maintenance hangar.
Multi-person AR/wearable supported aircraft maintenance.

The Individual Research Project prepares students for their chosen career. This is a structured programme for the student to demonstrate his/her ability to conduct original investigations, to test ideas and to obtain appropriate conclusions from the work. Our industry partners sponsor and support practical projects that meet their business needs. Research focus projects could be agreed for students seeking academic career.

For part-time students it is common that their research thesis is undertaken in collaboration with their place of work.

Example projects include:

Monitoring robotic vehicle movements using precision location system.
Aircraft damage detection using hangar surveillance cameras.
Lean operations in the hangar of the future.
Parked aircraft maintenance programme.
UAV operations and maintenance management system.

Modules

Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.

Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

Aim	To assess the current state and future opportunities for aviation digitalisation.
Syllabus	Aviation industry business stakeholders and dynamics. Aviation safety and airworthiness. Aircraft major structure and systems. Aviation operations, engineering and maintenance. E-enabled aircraft. Aviation digital transformation case study. Future air systems – UAV, eVTOL, integrated transport systems, commercial space operations. Sustainable aviation.
Intended learning outcomes	On successful completion of this module, you will be able to: Appraise the roles of various stakeholders in the aerospace ecosystem. Examine the safety and regulatory framework in the aviation industry. Distinguish the major aircraft components and systems. Categorise aviation operations and maintenance tasks. Assess the digital opportunities in aviation business transformation.

Aim

Syllabus

Aviation industry business stakeholders and dynamics.
Aviation safety and airworthiness.
Aircraft major structure and systems.
Aviation operations, engineering and maintenance.
E-enabled aircraft.
Aviation digital transformation case study.
Future air systems – UAV, eVTOL, integrated transport systems, commercial space operations.
Sustainable aviation.

Intended learning outcomes

On successful completion of this module, you will be able to:

Appraise the roles of various stakeholders in the aerospace ecosystem.
Examine the safety and regulatory framework in the aviation industry.
Distinguish the major aircraft components and systems.
Categorise aviation operations and maintenance tasks.
Assess the digital opportunities in aviation business transformation.

Aim	Introduce the underlying principles of data-centric engineering and associated data-driven computational and AI techniques that help build highly reliable, resilient and robust aircraft systems.
Syllabus	Overview of critical aircraft systems. Data-centric engineering – principles, strategies, and methodologies. Digital technologies and data-centricity. Data-driven computational predictive analytics. Sensing and instrumentation. Data mining and visualisation. Data-centric design. Intelligent diagnostics and prognostics for critical aircraft systems. Data-centric engineering for intelligent aircraft system design. Data-centric engineering based electrification modelling: more-electric aircraft.
Intended learning outcomes	On successful completion of this module, you will be able to: Examine the concept of data-centric engineering in the optimal design and through-life maintenance and health management of aircraft systems. Evaluate and leverage the technologies (such as the Sensors selection, Instrumentation, Analytics and Reasoners) that enable the capture of an ever-greater volume of data from heterogeneous sources and design safer, secure, and resilient aircraft systems. Evaluate techniques (including Machine Learning and Artificial Intelligence applications) to analyse data and gain insights for enhancing robustness and hence the overall aircraft system performance. Examine data-centric design concepts for through-life management of aircraft systems. Examine and compose realistic transformative plans and strategies for the development of novel data-engineered and data-driven systems, leading to more-intelligent aircraft systems.

Aim

Syllabus

Overview of critical aircraft systems.
Data-centric engineering – principles, strategies, and methodologies.
Digital technologies and data-centricity.
Data-driven computational predictive analytics.
Sensing and instrumentation.
Data mining and visualisation.
Data-centric design.
Intelligent diagnostics and prognostics for critical aircraft systems.
Data-centric engineering for intelligent aircraft system design.
Data-centric engineering based electrification modelling: more-electric aircraft.

Intended learning outcomes

On successful completion of this module, you will be able to:

Examine the concept of data-centric engineering in the optimal design and through-life maintenance and health management of aircraft systems.
Evaluate and leverage the technologies (such as the Sensors selection, Instrumentation, Analytics and Reasoners) that enable the capture of an ever-greater volume of data from heterogeneous sources and design safer, secure, and resilient aircraft systems.
Evaluate techniques (including Machine Learning and Artificial Intelligence applications) to analyse data and gain insights for enhancing robustness and hence the overall aircraft system performance.
Examine data-centric design concepts for through-life management of aircraft systems.
Examine and compose realistic transformative plans and strategies for the development of novel data-engineered and data-driven systems, leading to more-intelligent aircraft systems.

Aim	To assess the current state and future opportunities for digitalisation of aviation operations and maintenance management.
Syllabus	Fleet and operations planning. Aircraft engineering, maintenance planning and execution. Continuous airworthiness maintenance organisation. Digital systems for operations and maintenance management. Digital twins for operations and scenario simulation. Business transformation projects.
Intended learning outcomes	On successful completion of this module, you will be able to: 1. Construct aircraft fleet and operations planning process models. 2. Evaluate the different approaches to aircraft maintenance planning. 3. Appraise airworthiness requirements for information management. 4. Formulate a digital twin to represent through-life support scenario planning. 5. Prepare an aviation digitalisation business transformation project.

Aim

Syllabus

Fleet and operations planning.
Aircraft engineering, maintenance planning and execution.
Continuous airworthiness maintenance organisation.
Digital systems for operations and maintenance management.
Digital twins for operations and scenario simulation.
Business transformation projects.

Intended learning outcomes

On successful completion of this module, you will be able to:

1. Construct aircraft fleet and operations planning process models.
2. Evaluate the different approaches to aircraft maintenance planning.
3. Appraise airworthiness requirements for information management.
4. Formulate a digital twin to represent through-life support scenario planning.
5. Prepare an aviation digitalisation business transformation project.

Aim	This module aims to provide a systematic understanding and knowledge of key concepts and principles for digital engineering and its current practices, tools and processes and future development. The course will also provide hands-on experience using digital engineering tools and methods using examples from product and service development.
Syllabus	• Introduction to Digital Engineering concepts. • Digital Engineering Tools and Methods. • Internet of Things (IoT), Virtual and Augmented Reality (VR&AR). • Digital Twins. • Artificial intelligence and machine learning. • Digital Engineering Industrial Case Studies.
Intended learning outcomes	On successful completion of this module, you will be able to: 1. Evaluate the principles of digital engineering, its applications and benefits in product and service development. 2. Critically evaluate the selection of digital engineering tools and methods. 3. Create digital engineering tools and techniques in areas such as product and service development. 4. Critically evaluate the process of developing and using Virtual and Augmented Reality (VR&AR) methods and tools. 5. Evaluate the challenges in digital engineering implementation in industry.

Aim

This module aims to provide a systematic understanding and knowledge of key concepts and principles for digital engineering and its current practices, tools and processes and future development. The course will also provide hands-on experience using digital engineering tools and methods using examples from product and service development.

Syllabus

• Introduction to Digital Engineering concepts.

• Digital Engineering Tools and Methods.

• Internet of Things (IoT), Virtual and Augmented Reality (VR&AR).

• Digital Twins.

• Artificial intelligence and machine learning.

• Digital Engineering Industrial Case Studies.

Intended learning outcomes

On successful completion of this module, you will be able to:

1. Evaluate the principles of digital engineering, its applications and benefits in product and service development.

2. Critically evaluate the selection of digital engineering tools and methods.

3. Create digital engineering tools and techniques in areas such as product and service development.

4. Critically evaluate the process of developing and using Virtual and Augmented Reality (VR&AR) methods and tools.

5. Evaluate the challenges in digital engineering implementation in industry.

Aim	The aim of this module is to provide an introduction to predictive maintenance technology used in the aviation 鈥� transport sector, as well as to deliver cases for both industrial and research orientated applications.
Syllabus	Introduction to maintenance strategies. Introduction to structural health monitoring: principles and examples. Introduction to condition monitoring: principles and examples. Methods and tools for predictive maintenance. Signal processing and applications. Overview of signal processing software. Data analytics and data visualisation. Overview of data analytics and data visualisation software. Pattern recognition, neural networks and applications. Overview of pattern recognition software. Digital twins of aircraft systems in predictive maintenance. Prognostics. Practical case study: design and development of a condition monitoring system.
Intended learning outcomes	On successful completion of this module, you will be able to: 1. Evaluate basic principles in maintenance and key types of maintenance strategies. 2. Appraise the basic principles in machine dynamics and condition monitoring. 3. Appraise diagnostics and prognostics in structural health monitoring. 4. Appraise signal processing, data analytics and pattern recognition techniques used in condition monitoring and structural health monitoring. 5. Assess the role of Digital Twins & AI in predictive and preventive maintenance.

Aim

The aim of this module is to provide an introduction to predictive maintenance technology used in the aviation 鈥� transport sector, as well as to deliver cases for both industrial and research orientated applications.

Syllabus

Introduction to maintenance strategies.
Introduction to structural health monitoring: principles and examples.
Introduction to condition monitoring: principles and examples.
Methods and tools for predictive maintenance.
Signal processing and applications.
Overview of signal processing software.
Data analytics and data visualisation.
Overview of data analytics and data visualisation software.
Pattern recognition, neural networks and applications.
Overview of pattern recognition software.
Digital twins of aircraft systems in predictive maintenance.
Prognostics.
Practical case study: design and development of a condition monitoring system.

Intended learning outcomes

On successful completion of this module, you will be able to:

1. Evaluate basic principles in maintenance and key types of maintenance strategies.
2. Appraise the basic principles in machine dynamics and condition monitoring.
3. Appraise diagnostics and prognostics in structural health monitoring.
4. Appraise signal processing, data analytics and pattern recognition techniques used in condition monitoring and structural health monitoring.
5. Assess the role of Digital Twins & AI in predictive and preventive maintenance.

Aim	The aim of this module is to provide you with an introduction to the various aerospace and inspection monitoring tools and systems used, as well as to deliver cases for both industrial and research orientated applications.
Syllabus	Introduction to NDT imaging techniques for aircraft inspection. Localisation and mapping. Safe flight control and obstacle avoidance. Aerial contact based inspection. Aerial non-contact based inspection. Single and multi UAV path planning for inspection and monitoring. Damage detection and NDT sensing. Defect localisation, monitoring and image processing. Use of AI and ML for damage assessment and decision making. Maintenance and monitoring studies and applications. Practical case study 1: flying arena UAV testing for structural inspection. Practical case study 2: testing on an actual aircraft - 737.
Intended learning outcomes	On successful completion of this module, you will be able to: Design autonomous path planning for optimal remote structural inspection, including self-localisation within complex environments. Evaluate obstacle avoidance and aerial-ground robotic manipulation and inspection for monitoring and diagnostics. Evaluate principles of damage detection – non-destructive testing (NDT), sensing systems for maintenance and monitoring. Appraise damage detection applications, defect localisation, image processing and NDT assessment. Assess AI & image processing tools for automated defect recognition (ADR).

Aim

The aim of this module is to provide you with an introduction to the various aerospace and inspection monitoring tools and systems used, as well as to deliver cases for both industrial and research orientated applications.

Syllabus

Introduction to NDT imaging techniques for aircraft inspection.
Localisation and mapping.
Safe flight control and obstacle avoidance.
Aerial contact based inspection.
Aerial non-contact based inspection.
Single and multi UAV path planning for inspection and monitoring.
Damage detection and NDT sensing.
Defect localisation, monitoring and image processing.
Use of AI and ML for damage assessment and decision making.
Maintenance and monitoring studies and applications.
Practical case study 1: flying arena UAV testing for structural inspection.
Practical case study 2: testing on an actual aircraft - 737.

Intended learning outcomes

On successful completion of this module, you will be able to:

Design autonomous path planning for optimal remote structural inspection, including self-localisation within complex environments.
Evaluate obstacle avoidance and aerial-ground robotic manipulation and inspection for monitoring and diagnostics.
Evaluate principles of damage detection – non-destructive testing (NDT), sensing systems for maintenance and monitoring.
Appraise damage detection applications, defect localisation, image processing and NDT assessment.
Assess AI & image processing tools for automated defect recognition (ADR).

Aim	To assess the current state and future opportunities for digitisation of aviation supply chain management.
Syllabus	Aviation spares and materials management. Reliability engineering and integrated logistics support. Supply chain modelling and logistics simulation. Asset identity and life management – blockchain, etc. In-situ spares technology - 3D printed parts, etc. Business plan for supply chain transformation.
Intended learning outcomes	On successful completion of this module, you will be able to: Appraise the different types of aviation spares and airworthiness control. Estimate through life inventory cost impact of different operations and maintenance planning policies. Construct aviation supply chain simulation models. Evaluate business implication of emerging aviation supply chain technologies. Prepare aviation supply chain digital transformation project.

Aim

To assess the current state and future opportunities for digitisation of aviation supply chain management.

Syllabus

Aviation spares and materials management.
Reliability engineering and integrated logistics support.
Supply chain modelling and logistics simulation.
Asset identity and life management – blockchain, etc.
In-situ spares technology - 3D printed parts, etc.
Business plan for supply chain transformation.

Intended learning outcomes

On successful completion of this module, you will be able to:

Appraise the different types of aviation spares and airworthiness control.
Estimate through life inventory cost impact of different operations and maintenance planning policies.
Construct aviation supply chain simulation models.
Evaluate business implication of emerging aviation supply chain technologies.
Prepare aviation supply chain digital transformation project.

Aim	Introduce the underlying principles of security associated with avionics and communication technologies, further exploring industrial design approaches to secure connected aircraft.
Syllabus	Overview of communication and electronics technologies within the context of aerospace. Avionics cybersecurity, threats and countermeasure. Electronics supply chain; the root of trust vs security breaches. Design approach for building secured avionics. Advancing aircraft interface device functionality for protecting aircraft systems from connected electronic flight bugs. Aircraft communications addressing and reporting system (ACARS) network and the ACARS over IP concept. Best practices associated with protecting connected aircraft systems from potential threats. Practicing tools such as Wind River's Titanium Security Suite for extending embedded security specific to connected systems. Securing E-enabled aircraft. Industry response to new security regulations, vulnerabilities facing suppliers.
Intended learning outcomes	On successful completion of this module, you will be able to: Examine technologies addressing aerospace-related communication systems, local area networks for aircraft, embedded systems and avionics. Evaluate the avionics ecosystem concerning cybersecurity threats associated with communication, hardware, software, and supply chain attacks. Evaluate tools and techniques for building cyber-secured, avionics and communication systems. Assess avionics and communications suppliers' security approaches against current security regulations, standards, and certifications.

Aim

Introduce the underlying principles of security associated with avionics and communication technologies, further exploring industrial design approaches to secure connected aircraft.

Syllabus

Overview of communication and electronics technologies within the context of aerospace.
Avionics cybersecurity, threats and countermeasure.
Electronics supply chain; the root of trust vs security breaches.
Design approach for building secured avionics.
Advancing aircraft interface device functionality for protecting aircraft systems from connected electronic flight bugs.
Aircraft communications addressing and reporting system (ACARS) network and the ACARS over IP concept.
Best practices associated with protecting connected aircraft systems from potential threats.
Practicing tools such as Wind River's Titanium Security Suite for extending embedded security specific to connected systems.
Securing E-enabled aircraft.
Industry response to new security regulations, vulnerabilities facing suppliers.

Intended learning outcomes

On successful completion of this module, you will be able to:

Examine technologies addressing aerospace-related communication systems, local area networks for aircraft, embedded systems and avionics.
Evaluate the avionics ecosystem concerning cybersecurity threats associated with communication, hardware, software, and supply chain attacks.
Evaluate tools and techniques for building cyber-secured, avionics and communication systems.
Assess avionics and communications suppliers' security approaches against current security regulations, standards, and certifications.

Aim	The aim of this module is to provide a solid understanding of key concepts and principles for computational tools that are applied on aviation including current practices and future trends. The course includes hands-on practical's using representative cases from the aviation industry.
Syllabus	- Introduction to informatics concepts. - Applied computing tools and methods (in MATLAB and Python). - Shallow machine learning methods. - Data modelling and data analysis. - Pattern recognition and object detection. - Aviation Applied Computing Case Studies.
Intended learning outcomes	On successful completion of this module you should be able to: 1. Assess the principles underpinning applied computing, recognising its applications and advantages in both product and service development for the aviation sector. 2. Formulate critical assessments regarding the choice of computational tools and methodologies for the aviation sector. 3. Demonstrate proficiency in developing computational tools and techniques applicable to the aviation sector, including vehicle health management. 4. Critically analyse the procedures involved in the development and use of shallow machine learning, data analysis/modelling, and computer vision methodologies and tools. 5. Appraise the obstacles and code performances encountered during the implementation of computational tools within industrial settings by comparing two different implementation languages.

Aim

The aim of this module is to provide a solid understanding of key concepts and principles for computational tools that are applied on aviation including current practices and future trends. The course includes hands-on practical's using representative cases from the aviation industry.

Syllabus

- Introduction to informatics concepts.
- Applied computing tools and methods (in MATLAB and Python).
- Shallow machine learning methods.
- Data modelling and data analysis.
- Pattern recognition and object detection.
- Aviation Applied Computing Case Studies.

Intended learning outcomes

On successful completion of this module you should be able to:

1. Assess the principles underpinning applied computing, recognising its applications and advantages in both product and service development for the aviation sector.
2. Formulate critical assessments regarding the choice of computational tools and methodologies for the aviation sector.
3. Demonstrate proficiency in developing computational tools and techniques applicable to the aviation sector, including vehicle health management.
4. Critically analyse the procedures involved in the development and use of shallow machine learning, data analysis/modelling, and computer vision methodologies and tools.
5. Appraise the obstacles and code performances encountered during the implementation of computational tools within industrial settings by comparing two different implementation languages.

Teaching team

You will be taught by Cranfield's experienced academic staff. Our staff are practitioners as well as tutors. Knowledge gained working with our clients is continually fed back into the teaching programme, to ensure provision of durable and transferrable skills practised on problems relevant to industry. Additionally, experienced members of the Industrial Advisory Board deliver industrial seminars in which they share their experience and explain the research and development proprieties of their companies. The Course Director for this programme is Dr Ip-Shing Fan.

Accreditation

Accredited by BCS, The Chartered Institute for IT for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional and Accredited by BCS, The Chartered Institute for IT on behalf of the Engineering Council for the purposes of partially meeting the academic requirement for a Chartered Engineer.

This course is accredited with the BCS until August 2028. Candidates must hold a CEng-accredited BEng/BSc (Hons) undergraduate first degree to comply with full CEng registration requirements.

Cranfield’s new MSc in Aviation Digital Technology Management will provide industry with the skills needed to develop integrated digital systems capability to support aircraft operations.

Not only is this key for MROs and airlines but also for the growing number of technology companies that provide ERP solutions for aircraft operations. Lots of good solutions exist within airlines and MROs that address elements of operations (traffic, movements, status, procurement, maintenance management, quality, safety etc) but the future opportunity is integrated solutions at the industry level. This MSc will help develop people who understand the ecosystem of aviation operations and the systems requirements needed to create innovative solutions for the future.

I think DARTeC is hugely exciting for Cranfield. Aerospace is absolutely at the heart of what we do as an institution and DARTeC brings together the many different disciplines across the university. This is a really strong example of where all the university's strengths converge to solve one of society's challenges.

Your career

Industry-led education makes Cranfield graduates some of the most desirable all over the world for recruitment by both global primes to smaller innovative start-ups looking for the brightest talent.

Graduates of the course should find engineering and management opportunities in the aviation ecosystem, including operators, maintenance organisations, financiers, airports and future spaceport operators.

Graduates will be equipped with the advanced skills which could be applied to the aviation, air traffic, air transport, security, defence, and aerospace industries. This approach offers you a wide range of career choices in current and emerging roles. Others decide to continue their education through PhD studies available within 麻豆传媒AV or elsewhere.

Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. Support continues after graduation and as a Cranfield alumnus, you have free life-long access to a range of career resources to help you continue your education and enhance your career.

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