Acoustic Data Science Engineer (probes) / Ingénieur En Data Science Acoustique (probes)

GE HealthCare UK

Valbonne, France
Not specified
Ultrasound experience in acoustics or measurements
Transducer design development testing or modeling
Supervised unsupervised and deep learning methods
GE HealthCare UK is seeking an Acoustic Data Science Engineer to apply machine learning methods in the design and manufacturing of ultrasound probes. The role emphasizes collaboration within a multidisciplinary team and aims to enhance the efficiency and predictive capabilities of ultrasound transducers

Job Summary

  • This position applies machine learning methods to improve the efficiency of ultrasound transducer design and manufacturing processes.
  • The successful candidate will define high-level project requirements and build roadmaps for new and existing ML products.
  • The role offers a unique opportunity to utilize ML within a multidisciplinary team connected globally across Europe, Asia, and the USA.

Matching Summary

Match Score: 85

GE HealthCare UK is seeking an Acoustic Data Science Engineer to apply machine learning methods in the design and manufacturing of ultrasound probes. The role emphasizes collaboration within a multidisciplinary team and aims to enhance the efficiency and predictive capabilities of ultrasound transducers.

Skills & Requirements

Must-have

  • Ultrasound experience in acoustics or measurements
  • Transducer design development testing or modeling
  • Supervised unsupervised and deep learning methods
  • Signal processing classification feature extraction
  • Cloud deployment pipelines architecture knowledge
  • MATLAB Python PyTorch SQL Git tools

Nice-to-have

  • Strong oral and written communication skills
  • Ability to work autonomously in global teams
  • Experience with simulation-based training
  • Familiarity with Azure ML AWS SageMaker
  • Demonstrated problem analysis and resolution skills

Key Requirements

  • University degree in engineering acoustics physics or applied mathematics
  • Master's or PhD preferred
  • Experience applying ML to physical systems or hardware

Work Rights

Not specified

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