Master Thesis: AI-Driven RF Production Test Intelligence

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Analysis · Software Engineering

In short

This master thesis project focuses on applying AI and machine learning to large-scale RF production test data to derive actionable engineering insights. You will explore areas such as anomaly detection, root cause analysis support, quality risk prediction, and test coverage optimization, culminating in a proof-of-concept implementation.

Responsibilities

Analyze large-scale RF production test datasets.
Identify key RF performance indicators and manufacturing parameters.
Develop methods for data cleansing, normalization, and feature extraction.
Investigate machine learning approaches for detecting abnormal test behavior.
Identify outlier devices and unusual production trends.
Correlate RF test results with manufacturing information, component lots, test stations, environmental conditions, and other available factors.
Explore how AI can assist engineers in narrowing down potential root causes.
Develop explainable analysis methods to improve engineering confidence.
Investigate predictive models for identifying potential quality risks before products leave manufacturing.
Study early indicators of future failures or yield degradation.
Analyze relationships between different production test steps.
Identify redundant measurements and opportunities to streamline testing.
Evaluate potential reductions in test time while maintaining product quality and fault coverage.
Develop intuitive visualizations and dashboards for production data exploration.
Present AI-generated insights in a way that supports engineering decision-making.
Build and evaluate a proof-of-concept AI solution using real production data.
Demonstrate the ability to detect anomalies, provide engineering insights, and support production optimization activities.

Requirements

Knowledge in Computer Science, Electrical Engineering, or a related field.
Knowledge of data visualization and statistical analysis.
Familiarity with RF systems, wireless communication, electronics, or manufacturing processes.
Experience in at least one programming language (Python not mandatory).
Eagerness to learn and explore new concepts.
Bonus: experience in machine learning.

Desired Qualifications

Experience in machine learning is a bonus.

Benefits

Outstanding opportunity to use your skills and imagination to push the boundaries of what's possible.
Chance to build solutions never seen before to some of the world's toughest problems.
Be challenged but not alone; join a team of diverse innovators.
Encouraging a diverse and inclusive organization is core to our values.

Skills

PythonData VisualizationStatistical AnalysisMachine LearningRF systemsWireless CommunicationElectronicsManufacturing Processes

#AI#Machine Learning#RF Production Test#Data Analysis#Anomaly Detection#Root Cause Analysis#Quality Risk Prediction#Test Coverage Optimization#Data Visualization#Engineering Insights

About Ericsson

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