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Leonardo

Aerospace, defense tech design and manufacturing

Smart Maintenance and Predictive Analytics Intern

Data ScienceUpdated on Nov 10
Internship · Spring 2027Ortucchio, Italy€4.62 /hr

About the Job

Requirements

  • The candidate must be pursuing a master's degree in Electrical Engineering, Industrial Engineering (including Energy, Mechanical, or Electronics tracks), Automation, Computer Science, or a related engineering discipline.
  • The candidate must have written and spoken English proficiency at B1 level.
  • The candidate must have foundational knowledge of electrical engineering, electrical machines, and/or industrial systems.
  • The candidate must have foundational knowledge of mathematical analysis, engineering mathematical models, statistics, and data processing.
  • The candidate must have foundational programming and data analysis knowledge, preferably using MATLAB or Python.
  • The candidate must have foundational knowledge of automation and systems theory, including modeling relationships between inputs, operating states, and outputs of a physical system.
  • The candidate must be able to interpret operating measurements, nominal conditions, functional limits, and deviations from expected behavior.

Responsibilities

  • Study the operation of PILLER vertical-axis RUPS dynamic UPS systems and identify significant measurements, with possible additional study of Caterpillar/CGT diesel generator sets.
  • Analyze, clean, synchronize, and normalize time series extracted from the Emerson Ovation distributed control system.
  • Define mathematical and functional models of expected behavior under different operating conditions.
  • Develop multivariate analysis and deviation and anomaly detection algorithms, preferably in MATLAB.
  • Identify health indicators and potential degradation, including before conventional thresholds are exceeded.
  • Correlate results, where possible, with operational events, anomalies, failures, and maintenance tickets in the Operation & Maintenance system.
  • Validate the prototype using available data, assess its predictive limitations, and document results.
  • Model the operational and dynamic behavior of vertical-axis RUPS systems.
  • Analyze physical measurements and historical operating data from the Emerson Ovation distributed control system.
  • Develop algorithms for anomaly identification and degradation assessment.
  • Perform multivariate analysis and time-series analysis, and compare expected with measured behavior.
  • Correlate findings with operational events and maintenance records in the Operation & Maintenance system.
  • Validate results and produce technical documentation and automated reports.

Desired Qualifications

  • Familiarity with MATLAB/Simulink and data analysis and modeling tools.
  • Knowledge of dynamic system identification, control algorithms, and diagnostics.
  • Knowledge of machine learning, time-series analysis, and predictive maintenance.
  • Familiarity with industrial control systems such as PLCs and DCS, instrumentation sensors, and data acquisition.
  • Knowledge of IoT protocols and technologies for industrial monitoring.
  • Knowledge of reliability, diagnostics, and maintenance of industrial machinery and plants.

About the Company

Leonardo

Aerospace, defense tech design and manufacturing

Leonardo operates in aerospace, defense, and security, designing and manufacturing advanced technologies for militaries, intelligence agencies, and defense contractors. Its products combine hardware, software, and services and are delivered through subsidiaries in the US (DRS, Electronics US, Critical Infrastructure, Aircraft and Aerostructures US). It differentiates itself with a broad, diversified portfolio and end-to-end support from development to sustainment, plus training and customer service. Its goal is to provide reliable, state-of-the-art capabilities to defense, intelligence, and civil infrastructure customers while maintaining strong support and training.

Company size
10,001+
Company stage
IPO
Headquarters
Rome, Italy
Founded
2022

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