Acoustic Data Science Engineer (probes) / Ingénieur En Data Science Acoustique (probes)
GE HealthCare UK
Valbonne, France
On-site
Ultrasound transducer design and development
Machine learning methods for signal processing
Model evaluation and validation strategies
GE HealthCare UK is seeking an Acoustic Data Science Engineer in Valbonne, France, to leverage machine learning for improving ultrasound transducer design, development, and manufacturing processes. The ideal candidate should possess experience in acoustics and machine learning, with strong communication skills and the ability to work in a multidisciplinary, global team
Job Summary
Apply machine learning methods to ultrasound transducer design, development, and manufacturing processes.
Contribute to projects that improve efficiency in transducer design and manufacture, and perform predictive performance monitoring.
Work within a multidisciplinary team connected globally to colleagues throughout Europe, Asia, and the USA.
Matching Summary
Match Score: 85
GE HealthCare UK is seeking an Acoustic Data Science Engineer in Valbonne, France, to leverage machine learning for improving ultrasound transducer design, development, and manufacturing processes. The ideal candidate should possess experience in acoustics and machine learning, with strong communication skills and the ability to work in a multidisciplinary, global team.
Skills & Requirements
Must-have
Ultrasound transducer design and development
Machine learning methods for signal processing
Model evaluation and validation strategies
Cloud deployment with pipelines
MATLAB and Python development
Nice-to-have
Experience in acoustics or ultrasound measurements
Familiarity with simulation-based training
Experience developing visualization tools
Key Requirements
University degree in engineering, acoustics, physics, or applied mathematics
Masters or PhD preferred
Experience in ultrasound transducer design/development, testing, or modeling
Strong understanding of supervised, unsupervised, and deep learning methods
Experience developing and deploying models for signal-processing tasks
Understanding of code deployment on cloud environment with pipelines