Intern, Electrical Department – Equipment and Technology

European Space Agency - ESA

. Analyse and characterise space atomic clocks, including compact-clock anomalies, frequency jumps, and correlations with housekeeping telemetry parameters .

Posted 10/31/2026internshipNoordwijk • NetherlandsEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong analytical skills in characterizing space atomic clocks and applying machine learning techniques for anomaly detection. Proficient in signal processing, data analysis, and visualization, with experience in high-level programming languages such as Python and MATLAB.

Highest-signal resume keywords

Signal ProcessingData AnalysisMachine Learning TechniquesPython ProgrammingMATLAB Programming

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Hard Skills

Data AnalysisSignal ProcessingTime-Series AnalysisProbabilityStatisticsAnomaly DetectionData VisualizationMeasurement Data ProcessingScientific ComputingTest Automation

Soft Skills

Good Knowledge of EnglishGood Knowledge of French

Industry Keywords

Space Atomic ClocksTelemetryEnvironmental FactorsRadiometer InstrumentsIntegrated MixerMultiplier PerformanceCircuit TopologiesWaveguide CouplingThermal-Software IntegrationExport Control Regulations

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Analyse and characterise space atomic clocks, including compact-clock anomalies, frequency jumps, and correlations with housekeeping telemetry parameters
Work with real measurement data from long-term laboratory and environmental test campaigns
Compare observed clock behaviour against existing in-house clock models
Post-process and visualise time and frequency data from conventional space-qualified atomic clocks and low-SWaP timing technologies
Identify trends, recurring patterns, frequency excursions, and deviations from expected clock behaviour
Assess correlations between clock performance and operational or environmental factors
Investigate data-driven and machine-learning methods for anomaly detection, classification, and prediction
Develop tools and methodologies supporting automated analysis of large telemetry and frequency datasets
For the THz topic, use AI and existing or published designs to improve integrated mixer and multiplier performance
Investigate novel circuit topologies, waveguide coupling, non-linear device models, and potential thermal-software integration
Contribute to future optimisation of radiometer instruments and higher-frequency transmit/receiver development

Requirements

What you’ll need

Must be a university student, preferably in the final or second-to-last year of a Master's-level university course
Must be enrolled at the university for the entire duration of the internship
Good knowledge of English or French required
Knowledge of another ESA Member State language is an asset
Good knowledge of signal processing, data analysis, probability and statistics
Prior experience with high-level programming languages such as Python and MATLAB
Knowledge of time-series analysis and measurement data processing is an asset
Experience with scientific computing and data visualisation tools is an asset
Familiarity with measurement systems, instrumentation or test automation is an asset
Knowledge of Machine Learning techniques is an asset
Must be eligible to access information, technology, and hardware subject to European or US export control and sanctions regulations
Applications considered only from nationals of Austria, Belgium, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom

Benefits

Comp & perks

Internship programme conditions apply as published on ESA’s careers website
Equal opportunity employer
Workplace support and recruitment assistance for individuals with disabilities, where possible