An international call to hire researchers at the Leonardo Labs
Leonardo is continuing to establish the Leonardo Labs, an international network of corporate laboratories dedicated to advanced research and technological innovation. The Leonardo Labs need outstanding, enthusiastic young researchers with a degree and/or a PhD in STEM disciplines, together with expertise in the following areas:
High performance computing/ Cloud
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- High Performance Computing: Porting, profiling, optimization and parallelization of scientific and industrial code on heterogeneous HPC infrastructures (CPUs, GPUs), development and implementation of new capabilities based on parallel algorithms and innovative computational methods;
- Hardware / Software Co-design: Validation and evaluation of scientific and industrial codes performance on different hardware architectures, either already existing or experimental (e.g. RISC-V processors), in the context of Supercomputing and Edge Computing;
- Cloud Computing: Instances creation and management on Cloud Computing infrastructures, Cloud services applications containerization, container deployment via orchestration tools, implementation of Data Lakes infrastructures.
To participate in the selection process, some of the following requirements are required:
- Experience working in Linux environments and High Performance Computing skills in code development and optimization (compilers, profilers, debuggers, git, cmake);
- Familiarity with methods for validating and evaluating the performance of scientific code, in particular parallel codes (e.g. strong and weak scaling);
- Knowledge of parallel programming algorithms and techniques in various hardware environments (shared memory, distributed memory, GPU acceleration);
- Knowledge of programming languages for scientific computing (C, C++, Fortran, Python), parallel programming paradigms on CPU and GPU (MPI, OpenMP, OpenACC, CUDA), languages for heterogeneous architecture (SYCL);
- Experience with Edge Computing paradigms and their methodologies;
- Familiarity with different application containerization methods (Docker, Singularity, Podman), container building and deployment;
- Knowledge of open source Cloud Computing platforms (OpenStack) and instances creation, management and deployment;
- Experience with Cloud orchestration tools to automate deployment, scaling and management of containers (Kubernetes, OpenShift);
- Familiarity with Data Lakes technologies for Data Analytics and related Cloud Services (e.g. Hadoop, Spark).
Optoelectronics
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Advanced electromechanical systems for optical applications
- Study and design of Metalenses for electro-optical systems
- Neuromorphic electro-optical vision
To participate in the selection process, some of the following requirements are required:
- Metalens technologies and design
- Electro-optical systems
- Modelling and simulation
- Scientific simulation using specific software (e.g. Matlab®, Phyton)
- Assessment and validation techniques
- Knowledge and design of systems based on neuromorphic vision sensors
- Vision sensors
- Knowledge of algorithms for processing images and extracting information from images and video
- Artificial Intelligence and deep learning techniques applied to machine vision
- Design of ultra-rapid miniaturised electro-mechanical control systems
Cyber
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Threat detection: Identifying complex attack patterns to improve detection, triage and attribution capabilities automatically suggesting the most effective defence strategies by improving the speed and effectiveness of cyber incident response processes.
- Cryptography: Study of innovative solutions in data protection domain broken down as data security (protection against theft/unauthorised access) and data resilience against events that can lead to loss or corruption (data vaulting). Implementation and verification of cryptographic protocols on systems that use encryption to address real-world security concerns.
- Active cyber defence: exploring methods, models, and use cases for building autonomous agents to be used in active defence activities also in a simulated environment.
To participate in the selection process, some of the following requirements are required:
- Good cyber security knowledge
- Excellent knowledge of Python programming
- Excellent knowledge of machine learning and data science frameworks: scikit-learn, pandas, seaborn, xgboost, lightgbm, catboost, pytorch/tensorflow/keras
- Good knowledge of algorithms and graph theory
- Good knowledge of Generative Adversarial Networks (GAN)
- Good knowledge of Deep Learning techniques based on Recurrent Neural Networks, Transformers and Decision Trees
- Excellent knowledge of time-series analysis forecasting techniques
- Excellent knowledge of written and spoken English
- Good knowledge of GIT versioning software
Artificial intelligence
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- General Purpose Deep Learning: applied to one or more of the following areas, Self-Supervised Learning, Domain Adaptation, Few-shot Learning, Continuous Learning, Reinforcement Learning, Generative Deep Learning
- Trustworthy AI: for Explainable AI, Model Robustness, Dataset Analysis, Certified AI
- Natural Language Processing: per svolgere task di Sentiment Analysis, Named Entity Recognition, Relation Extraction, Text Similarity, Text Summarization, Knowledge Graphs Construction
- Computer Vision: deep learning techniques for Image Recognition, Super Resolution, Object Detection, Hyperspectral Image Analysis, Satellite Image Analysis,
- Audio and Signal Analysis: Audio Analysis, AI applied to Radar Technologies, Time series Analysis
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Radar and radio communication systems: digital signal Processing algorithms and architectures, detection and estimation theory, cognitive and adaptive signal processing, software defined radio technology, data fusion techniques, phased array systems
To participate in the selection process, some of the following requirements are required:
- Bachelor/Master degree: Computer Engineering, Automation Engineering, Aerospace Engineering, Electronic Engineering, Data Science, Physics, Mathematics, Statistics or similar subjects
- Preferably a Master's and/or PHD in Machine Learning / Deep Learning / Data Science
- Advanced knowledge of Deep Learning, including: Neural Networks (e.g. CNN), Transformers (e.g. ViT/LLM), Recurrent Neural Networks (e.g. LSTM/GRU)
- Advanced knowledge of Python and Pytorch
- Good knowledge of written and spoken English
- Good teamwork skills
- Ability to manage research projects independently
Sustainability
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Battery recycling and end-of-life scenarios: extend the life cycle of critical battery components with the aim of pursuing the circular economy and avoiding the use of conflict minerals;
- Circularity of composite materials: identify new methods for recycling composite materials, with the aim of creating a supply chain that covers the entire recycling chain;
- Identifying new alternatives for chromium plating in industrial processes: Evaluate and identify industrial processes where the use of chromium plating can be eliminated;
- Products’ life cycle assessment: Develop the life cycle assessment method in the model of developing, deploying and managing the after-sales of new Leonardo products;
- Sustainable aviation fuel scenarios and tests: perform an analysis on the Leonardo product portfolio; run use cases on machines to certify aircraft flight using a mixture of SAF in increasing proportions;
- Green computing & green coding: improve the sustainability of the computing and coding activities of Leonardo through the optimization of proprietary codes, and open source codes;
- Efficiency of public transportation: exploit the data recorded in the equipment already installed in the bus fleet managed by leoanrdo, with the aim to improve thee local public transport and reduce the emission of CO2;
- Satellite data for water stress analysis: evaluate with deep insight the water risk on Leonardo business, based on available hydrologic and statistic models and software on the market.The objective is to provide the esteem of the water stress of Leonardo sites. Other activity with satellite data can be coduct in term of biodiversity evaluations.
To participate in the selection process, some of the following requirements are required:
- Knowledge of battery chemistry;
- Experience with new sustainable technologies for recycling/reusing/extending battery life;
- Knowledge of composite materials and their recycling;
- Knowledge of the composite material recycling chain;
- Knowledge of chromium plating and related industrial processes;
- Knowledge of Reach regulations;
- Experience with designing and using the LCA method;
- Good knowledge of Python programming;
- Ability to analyse the chemical composition of fuels;
- Experience engineering an aircraft-helicopter engine;
- Good knowledge on coding with particular regards on code optimization and code parallelization;
- Good knowledge on High Performance Computing (HPC) architectures and methodologies;
- Development of internal big data framework related application;
- Develop highly scalable microservices based cloud architecture;
- Knowledge of AI, algorithms and graph theory.
Materials
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Smart materials: application and fundamental research in self-healing materials; programmable materials; and self-sensing materials (sustainable piezoelectric materials).
- Multifunctional materials: fundamental investigation and applications of advanced sustainable composites enabled by natural and sustainable polymers, ceramics, metallic, fibers;sustainable coatings; nanomaterials (graphene, Mxene, borophene etc.); and metamaterials (design, optimization and testing based on application).
- Emerging energy materials: fundamental research in photovoltaic materials (solar cell efficiency, cell material design and testing); batteries (structural, novel material designs); fuel cell materials (catalysts, layer optimization, electrolytes etc.); high and low temperature superconductivity; hydrogen carrier materials (metal organic frameworks etc.); and thermal management materials.
- Additive manufacturing: research domains correlated to design for AM (Material Selection and Studies (Polymer, Metallic), Topology optimization, Printer Parameter Optimization, Process Optimization (FDM, SLA, PBF, DED, SLM, AFSD, WAAM)); metallic and polymer AM repair; continuous fiber AM.
- Advanced joining and repair: fundamental research in adhesion processes; novel welding processes; and molding techniques.
To participate in the selection process, some of the following requirements are required:
- Masters or PhD in Materials Science, Energy, Physics, Chemistry, Aerospace, Mechanical or similar with minimum 2 year of research experience in the applied research domain.
- Material, process or structural simulation/modeling at different scales (atomic to macro scale).
- Excellent knowledge of one or more of applied research domains.
- Experience with lab scale microscopy (optical and electron) and sample preparation (sectioning, cutting, grinding, polishing etching etc.).
- Experience with testing (mechanical/electronic/thermal) in different environments.
- Documented research experience with publication in high impact journals or patents and conference presentations for the applied research domain.
- Excellent coordination and teamwork capability. Dynamic and respectful of the international nature of the research group.
- Excellent knowledge of English, spoken and written.
- Sound knowledge in programming (preferably C++, Matlab, Python).
Space technologies
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Space robotics: development of robotic technologies for in-orbit services, e.g. in-orbit fuelling, remote debris return and planetary exploration. In particular, implementation of new solutions for tele-operation of remote systems with high delays in communications, locomotion, and low-gravity navigation and manipulation.
- Future Solar Generators: exploring new technologies such as floating photovoltaics, thermophotovoltaics, solar and rain panels, and perovskite solar cells
- Future Optical Instruments: new technologies for optical instruments used for spatial observations
- Space Domain Awareness (SDA) and Space Situational awareness (SSA): the ability to detect space objects, catalogue them, determine and predict their orbits; study solar activities and spatial environmental effects that may affect the performance and reliability of space and ground technology systems; collect data and information to identify unknown satellites, understand whether they are operational, and discover their capabilities and purposes.
To participate in the selection process, some of the following requirements are required:
- Astrodynamics and orbital mechanics
- Motion modelling relating to proximity operations in highly nonlinear dynamics
- Design and implementation of guidance, navigation and control algorithms to increase spacecraft autonomy
- Design and implementation of guidance and control algorithms for autonomous vehicles
- Generation of energy from renewable sources that can be used in space, e.g. hydrogen produced fro
Unmanned systems & robotics
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Autonomous Flight: Development of hardware and software for autonomous drones. In particular, the candidate will contribute to the implementation of perception, navigation, trajectory planning and control (flight and landing) algorithms on drone prototypes.
- Multi-Agent Planning: Development and implementation of a framework for coordinating smart autonomous systems (e. g. ground robots and UAVs) and for the autonomous performance of complex missions. In particular, the candidate will be responsible for optimal resource allocation (multi-objective optimisation) and information sharing between agent systems.
- Manned-Unmanned Teaming: Implementation of a framework for coordinating autonomous pilot-controlled systems for different land and air environments. The candidate will implement collaboration algorithms between manned and unmanned systems to help pilots achieve their tasks.
- Swarming Capability: Development of a framework for distributed control and for coordination and collaboration on a large number of autonomous platforms to achieve a common task. The candidate will implement collaboration algorithms between autonomous systems, optimal resource allocation, and information sharing between agent systems.
- Robotics for Manufacturing: Development of collaborative and industrial robotics intelligence frameworks. The candidate will develop software solutions for the efficient combination and collaboration of one or more fixed or mobile robotic manipulators and human operators, to meet the advanced agility and dexterity requirements of industrial manufacturing and logistics environments.
- Unstructured Environment: Development of robotic technologies, hardware and software to be applied in new domains with unstructured environments. In particular, the candidate will contribute to the development of prototype robots capable of self-navigating in indoor/outdoor environments, motion planning, object and terrain control and recognition.
- Space Robotics: Development of robotic technologies for in-orbit services, e.g. in-orbit fuelling, remote debris return and planetary exploration. In particular, the candidate will implement new solutions for tele-operation of remote systems with high delays in communications, locomotion, and low-gravity navigation and manipulation.
To participate in the selection process, some of the following points are required:
- Good programming skills (e.g. Python [e.g. numpy, pandas], C++), overall a good level of digital expertise, Linux (Ubuntu), real-time systems such as Xenomai, Preempt_RT are considered an advantage
- Knowledge of robotics- -oriented software such as ROS(1&2), ISAAC SIM, Gazebo, Air-Sim, , , px4 autopilot, ros control, etc.
- Knowledge of control and planning algorithms such as sample-based planning (e.g. RRT), optimal control(e.g. MPC), Behaviour Trees
- Knowledge and use of linear/non-linear optimisation algorithms
- Knowledge of navigation, localisation and 2D and/or 3D mapping software, e.g. gmapping, PCL, etc.
- Knowledge of vision and perception algorithms and use of e.g. VISP and/or data-driven software
- Good knowledge of software packages for machine learning / deep learning (e.g. Scikit-learn; pytorch, keras, tensorflow),and reinforcement learning (e.g. gym)
- Knowledge of algorithms for the control of manipulators and wheeled and legged systems, in particular forward/ inversekinematics, forward/ inversedynamics, impedance/admittance control
- Mechatronics control and design
- Ability to integrate different solutions system-wide
- knowledge of networks is considered an advantage
Big data analytics
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Digital Factory
- Predictive Maintenance
- Big Data Systems
To participate in the selection process, some of the following requirements are required:
- Design and development of data processing pipelines for batch and stream services
- Distributed computing in cloud platforms and big data domains (e.g. GCP, AWS, MS Azure, Hadoop, Spark), data mining, predictive modelling, machine learning, statistical modelling, large-scale data acquisition, transformation and cleansing (both structured and unstructured data)
- Cloud and container technologies (one or more): Kubernetes, OpenStack, Singularity, Docker
- Advanced programming: Python, C++/C, Spark, Hadoop, Hive, Cassandra, Mongo DB, Hibari, Redis
- SQL and NOSQL database design and programming (MySQL, MongoDB, MariaDB, SQLite, PostgreSQL)
- Software engineering: familiarity with GIT, AGILE methodology, Continuous Integration and Delivery
- I/O architectures: POSIX file systems, data encryption technologies, data protection technologies
- Data analytics and machine learning tools (one or more): Rapidminer, Pentaho, OpenRefine, Pandas; Linux and Workload Manager Operating Systems
- Development of IVHM/PHM solutions
- Development of data processing, feature engineering, anomaly detection, fault identification/isolation and diagnostics/prognostics models and algorithms for IVHM/PHM applications
- know-how in data management and the corresponding languages and tools (such as SQL)
- signal processing skills
- know-how in data analytics and machine learning tools, models and algorithms
- software development and programming skills (e.g. Python, C/C++/C#)
- knowledge of simulation software packages (such as Matlab/Simulink)
Digital twin & advanced simulation
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Computational designs based on multiphysics and multi-scale frameworks
- PyFR , Calculix and OpenFOAM developments
- Data driven approaches (e. g, physics informed neural networks)
- Linear algebra for deep learning, GPU optimizations and benchmarks on HPC
To participate in the selection process, some of the following requirements are required:
- Aerospace or Mechanical Engineering modeling and simulation background
- Knowledge of typical multi-domain commercial simulation software (e.g. Matlab-Simulink/Simscape, Simcenter Amesim, Dymola etc.)
- Experience with model-integration framework (e.g. FMU standard and similar)
- Knowledge of MBSE technique for Requirements tracking and functional-logical modeling
- Programming background with knowledge of Python and other high-level programming frameworks
- Programming background of software development tools (e.g. Git, Docker and front-end interfaces)
- Knowledge of Data management tools (e.g. Pandas, SQL, No-SQL etc.)
- Experience with Linux
Nice to have:
- experience with parallel programming on HPC infrastructure
- experience with Phoenix ModelCenter suite
Quantum Technologies
In this research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
- Quantum computing: identification of opportunities and challenges associated with quantum computing combined with high-performance digital computing capabilities to investigate potential applications and use cases in quantum machine-learning, optimization, and scientific simulation.
- Quantum sensing for position navigation and timing: atomic clocks, atom interferometry, laser cooling, magneto optical trapping, ion trapping, optical lattices, etc.
- Optical systems engineering: modelling, simulation and design of optical systems; experimental implementation and testing of optical systems (even with background experience on communications, spectroscopy, imaging systems or even systems for more complex and exotic applications)
- Quantum comms: quantum random number generators, free space or fiber based quantum key distribution, quantum vulnerability analysis for quantum communication systems, quantum networks or quantum internet concepts, etc.
- Quantum imaging: time-correlated single-photon counting, ghost imaging, quantum illumination, seeing-behind-the-corner systems
To participate in the selection process, some of the following requirements are required:
- Analysis, simulation and design of components, systems or networks
- Realization and characterization of experimental test beds
- Development of algorithms and theoretical analyses
- Quantum computing paradigms: quantum annealing, logic gates (gate-based) and measurements (measurement-based)
- High performance computing: parallel computing (MPI, openMP) and GPU computing acceleration
- Programming: C/C++, Fortran, CUDA and Python. Libraries for scientific computing, data analysis and visualization (numpy, scipy, pandas, mpi4py, cupy, matplotlib)
Further general requirements to participate in the selection process:
- Development, analysis, validation of algorithms and applications for NISQ Quantum computers
- Quantum computer emulation: knowledge and use of development software (qiskit, cirq, qibo, paddle-quantum, cuQuantum)
- Machine learning, deep learning and neural networks: open-source libraries (Tensorflow, Pytorch, Keras, scikit-learn)
- Hardware and middelware technologies for quantum computing platformsConducting R&D projects
- Preparation of funded project proposals for private or institutional clients
Electrification
In this area research area, Leonardo is looking for researchers who have gained scientific work experience in one or more of the following domains:
Helicopters
- Hybrid/electric propulsion systems for aircraft, helicopters or tilt-rotor craft
- Electrical machines for propulsion applications, engines, generators and inverters
- Electric batteries and high voltage distribution systems
- Diagnostics for electrical systems (engines, batteries)
- Electrification of the rotor of helicopter tails
- Contactless transfer/generation of energy
- Electrical and hybrid propulsion applied to new aeronautical platform configurations
- Retrofitting of aeronautical propulsion systems with hybrid solutions
- Fuel cell-based propulsion
- Hydrogen for propulsion applications and related systems (fuel tanks and systems
To participate in the selection process, some of the following requirements are required:
- Knowledge of the electrical systems and design principles of electric cars
- Familiarity with aeronautical propulsion systems
- Knowledge of the principles of design of aeronautical structures
- Knowledge of simulation software packages (such as Matlab/Simulink, Simcenter Amesim, structural FEM)
- Programming skills and a good level of overall digital skills
- Experience in system integration
- Good team and public communication skills
- Ability to work with multidisciplinary teams
- Good time management
- Excellent knowledge of English (written and spoken)
Aircraft
- Scaling, performance analysis, and trade-off studies for electrical distribution architecture onboard electric and hybrid/electric aircraft
- E.Power Management: supervision and control of micro-networks, also using innovative approaches (such as artificial intelligence)
- Implementation of measures (hardware and/or software) for optimal management of energy quality (i.e. voltage) onboard electric aircraft
- Modelling of electric, electronic and electromechanical devices using the Model-Based System Engineering approach
- Analysis of systems and protocols for interconnection and data transmission among electronic control units (e.g. CANbus / CANaerospace)
- Analysis of the stability and control of electrical feedback systems characterised by various rings of control
- Dependability analysis and hazard analysis applied to electrical systems onboard aircraft using qualitative approaches (e.g. FMECA) and quantitative approaches (e.g. FTA)
- Programming of Power Hardware In the Loop (PHIL) systems for electrical mobility
- Overall scaling, modelling and control of DCDC electronic power converters (insulated and uninsulated) running on voltages of more than one kilovolt
- Overall scaling, modelling and control of solid-state devices for protection against over-currents operating at voltages of more than one kilovolt
- Analysis of systems for controlling insulation of electrical networks operating through the earthed distribution scheme
- Modelling and control of multi-phase, multi-level active rectifiers and inverters for multi-megawatt applications
- Implementation of control schemes for multi-phase and multi-level electric drives
- Electric, thermal and mechanical modelling of devices for accumulating electric energy
- Thermal and mechanical modelling of devices for static and electromechanical conversion
- Scaling and analysis of innovative systems for thermal management of electrical components
- Scaling of wiring, connectors and other dielectric systems for operating voltages of more than one kilovolt operating at low environmental pressure
- Scaling and modelling of devices for the elimination of conducted and radiating electromagnetic interference
- Scaling and modelling of innovative Thermal Management systems
Requirements for participation in the selection process:
- Knowledge of electrical architectures for innovative aircraft propulsion and familiarity with the analysis and testing of electrical devices for the accumulation, conversion and distribution of electrical energy in complex networks
- Familiarity with the principal laboratory instruments (multimeter, oscilloscope, spectrum analyser, programmable power supplies, communication cards and protocols)
- Familiarity with the implementation of systems for the automation of electrical and electromechanical test benches (e.g. National Instrument / LabView, or similar)
- Familiarity with the principles of electrical safety and knowledge of the safety issues involved in handling high voltages
- Familiarity with generating code from models for implementation in hardware systems in the loop (e.g. Opal, DSpace, etc…)
- Mastery of the modelling and control of multi-phase and multi-level converters, machines and electrical drives through analytic and numerical approaches
- A solid technical background in dynamic analysis (in the time and frequency domain) of complex electrical systems (through software such as PLECS or equivalent)
- Familiarity with classic control applied to electrical systems (e.g. tuning regulators of position, speed, current, voltage, etc.)
- Mastery of modelling of high-power electrical and electromechanical systems using the model-based system engineering approach
- Familiarity with the principal systems modelling software (Matlab/simulink, Modelica, Simcenter Amesim)
- Familiarity with advanced programming and statistical data analysis tools (Python, R, Minitab, C++, C)
The collaboration contracts duration is up to 5 years, renewable, with European standard salary.
Leonardo has a gender policy in place: woman scientists are strongly encouraged to apply. We encourage candidates from around the world to apply.
Candidates should submit the application form and the updated CV to the following email address: LeonardoLabs@leonardo.com
For further info, contact us at LeonardoLabs@leonardo.com