Defence Technologies Roadmap and Skills Blueprint

Job Profiles, Technologies & Skills Roadmap

The ASSETs+ roadmap is built around the Human Resources, one of the fundamental capabilities, together with technological means, for the European autonomy and sovereignty. It includes the most relevant job profiles, the most relevant skills for each job profile and the most relevant technologies, emerging from the analysis done by the ASSETs+ project team.

The technologies are positioned around the job profiles, considering the importance of a technology for a given job, and along the radial dimension, considering the level of maturity of a technology. In this way, the technologies, the skills, and the job profiles are distributed in four main areas, that describe the most relevant capabilities for the Defence sector with particular reference to the technological domains of Robotics, Autonomous Systems, AI, Cybersecurity and C4ISTAR.

Click on the hotspots for more information about the most demanded SKILLS

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SKILLS

  • Apply Reverse engineering
  • Create A product virtual models
  • Using digital tools for processing sound and images
  • Human-robot Collaboration
  • Robot Programming
  • Systems Engineering including safety and security
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SKILLS

  • Design User Interface
  • Develop Software Prototype
  • Distributed Computing
  • Integration of 5G services with Cloud services
  • Coordinate Technical Standards for Global Interoperability
  • Computing system architecture
  • Open-Source Management
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SKILLS

  • Embedded Systems
  • Integrate Systems Components
  • Prepare & Apply Security Test Plans
  • Secure Networks Communications
  • Use Reservoir Surveillance
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SKILLS

  • Analyse Big Data
  • Computer Programming
  • Identify Data Supporting Strategies
  • Real Time Computing
  • Scientific Research Methodology

Description of the capabilities areas

Autonomous Systems

KEY FIGURES

  • Job profile: Aerospace Engineer
  • Skills: Apply Reverse Engineering; Create A Product’s Virtual Model; Using Digital Tools For Processing Sound And Images; Human-robot Collaboration; Robot Programming; Systems Engineering including safety and security
  • Technologies: Helicopter; Drones; Digital Twins; Autonomous Systems; Cyber Physical Systems; Intelligent Systems; Soft Robotics; Navigation and Vehicle Control System

Technologies

  • The design of digital twin of physical assets requires a detailed modelling of the information flow in two directions (in-out and vice versa).
  • The cognitive modelling can be used to simulate or predict human behaviour or performance on tasks like the ones modelled and improve Human Machine Interaction.
  • The design of the physical assets should consider the integration of AI, ML, cloud computing technologies, towards embedding AI on the edge.

Integration
Human workers and increasingly high-tech components – whether hard or soft – will inevitably interact and boost daily operations and missions.

Skills

  • Even if autonomous during the operations, these systems will require increased effort in their design and their maintenance.
  • The psycological impacts (decoupling, concentration issues) of using autonomous systems for defence must be managed.
  • Safety and security aspects will be critical for achieving trustworthy certifiable systems.
  • Monitor and contribute to development of Technical Standards and guidelines for use of AI in Defense and other high reliability and/or critical systems system (e.g., SAE G-34, Artificial Intelligence in Aviation, and SAE G-32 Cyber Physical Systems Security Committee)

Integration
Autonomous systems are related with the development in AI, expecially in deep neural networks and black box systems in general. Reaching a greater explainability in the AI generated models is criticall for the development of these systems.

Cybersecurity systems

KEY FIGURES

Job profile: Security Architect

Skills: Embedded Systems; Integrate System Components; Prepare and Apply
Security Test Plans; Secure Network Communications; Use Reservoir Surveillance

Technologies: Encryption; Firewalls; Intrusion Prevention Detection Systems
(IDS/IPS); Battlefield of Things, Deception Technology; 5G/6G; AI for Cybersecurity; Hardware and Software Security (incl. secure supply chain of Open Source and/or third-party software)

Technologies

  • Automate cybersecurity activities: leverage on AI applications to execute tasks (e.g., monitoring infrastructure, incident response management, traffic analysis) and enable smart decisions providing validation detection engine (mix of experts knowledge and data)
  • Broaden the perspective in systems protection strategies taking into account the vulnerability of the various actors in the network, whether they are open sources and vertical or horizontal along the supply chain.
  • Monitor the impact of quantum computing on cybersecurity. It is unknown, but likely high, so it will be a game-changer.

Integration
Experts in the field will use and increase their skills and knowledge to develop a security architecture that integrates all the high-tech elements that inevitably interact and boost daily operations and missions.

Skills

  • Cyber security is re-shaping the defence sector, creating new (virtual) spaces to defend, and thus new job profiles to manage these spaces.
  • These systems are the most challingengig pedagocilly speacking. To increase people skills in cyber secturity there exists the need to create real-life scenario, hard to be created considering data considentiality.

Integration
Autonomous systems are related with the development in AI, expecially in deep neural networks and black box systems in general. Reaching a greater explainability in the AI generated models is criticall for the development of these systems.

Intelligent Information Systems

KEY FIGURES

  • Job profile: Data Scientist
  • Skills: Analyze Big Data; Computer Programming; Identify Data Supporting Strategies; Real-time Computing; Scientific Research Methodology
  • Technologies: Voice Recognition; Natural Language Processing; Computer Vision; Deep Learning; Adversarial Machine Learning; Explainable AI; C4ISTAR Big Data

Technologies

  • Ensure that the AI algorithms are explainable and testable:
    – The algorithm should be described to enable the certification of the products.
    – The computational procedure should be assessed for the reliability of the results.
  • Combine multiple algorithms to solve complex problems and support decision making processes
  • Develop Technical Standards for AI in defence, dealing with model standardization, ethics, and confidentiality issues.

Integration
Data from various sources requires harmonization to be properly exploited in high-tech systems with AI and ML algorithms and to gather knowledge for informed decision and boost daily operations and missions.

Skills

  • State of the art AI technologies are open-source. This is a game changer process for the defence sector, which is istorically based on propietary and secret technologies. The sector need upskillinig in this direction.
  • Every AI technology is in fact considerable a dual use technology. This is opening the boundaries of the defence sector, and AI professionalls can easilly spend their skills in and out the sector.

Integration
AI systems are potential trheats for cyber security and as a conseguence valuable defence systems. Having the right AI competences in a company means been abelt o properly defend information.

High performance computing systems

KEY FIGURES

  • Job profile: Database Designer
  • Skills: Design User Interface; Develop Software Prototype; Distributed Computing; Embedded real-time systems; Integration Of 5G/6G Services with Cloud Services; Coordinate Technical Standards For Global Interoperability; Computing system architecture; Open Source Management
  • Technologies: Big Data; Edge Computing; High Performance Computing; Deep Neural Network (DNN) processor; AI accelerator; Quantum Computing; Fog Computing; Federated Learning; S/W Defined Infrastructure incl. Network; Containers, Virtualization and automation

Technologies

  • Embed AI on edge devices to elaborate directly the data, squeeze the model and the data on the edge to ease of data processing, avoid cybersecurity issues, ensure an easy transportation of the models, etc.
  • Distributed computing resources that provide real-time guarantees of resources when needed for system functionality.
  • AI/ML accelerators and System on Chip (SoC) devices including reconfigurable processing technology
  • Design space Exploration to map system functionality to available resources that guarantees correct computing and communication performance when needed.
  • Quantum Computing and Fog Computing are shaping the future of the computational architecture.

Integration
Improve the computational capacity and balance it with the power of the elaboration tools and methods. The servitization is the key to scale and properly exploited the technical solutions with data and AI/ML algorithms, to boost daily operations and missions.

Skills

  • Bringing the computation to the edge require new AI design skills beyond embedded software skills of today to manage efficient systems. Now most of AI engineer are used to think at quasi-infinte computantional power.
  • Safety and security aspects of the computing infrastructure will be critical for achieving high performance and energy efficient trustworthy certifiable systems, comprehensive combination of hardware and software knowledge will be required.
  • Quantum computing may have a huge impact on AI. What is doable now only by big clusters of serves (systems such as GPT-3) can be tomorrow done by a smartphone or any other device on the field. There is the need for skilled designers, able to imagine the systems of the future, and the potential impact of these systems in the sector.

Integration
High performance computers will impact all the ther fields. AI and robots systems will be reshaped by the new computational systems.