AI-Driven Ultrasonic Materials PhD (Fully Funded) in Brighton

AI-Driven Ultrasonic Materials PhD (Fully Funded) in Brighton

Brighton Full-Time 19350 - 23650 Β£ / year (est.) No working from home possible
Technical University of Denmark

At a Glance

  • Tasks: Conduct AI-driven ultrasonic evaluations and run large-scale simulations.
  • Company: The University of Sussex, a leading research institution.
  • Benefits: Fully funded PhD, access to cutting-edge resources, and collaboration with industry partners.
  • Other info: Exciting opportunity for career development in a dynamic research environment.
  • Why this job: Join a pioneering project that merges AI and materials science for real-world impact.
  • Qualifications: Strong background in materials science, AI, and programming skills.

The predicted salary is between 19350 - 23650 Β£ per year.

The University of Sussex invites applications for a fully funded PhD studentship focused on AI-driven ultrasonic evaluation of materials, addressing inverse models to extract material properties from measurements.

You will run large-scale ultrasound simulations, design neural networks, quantify uncertainty, and validate models in our ultrasonic laboratory, collaborating with industrial partners to bridge simulation and experiment.

AI-Driven Ultrasonic Materials PhD (Fully Funded) in Brighton employer: Technical University of Denmark

The University is an exceptional employer, offering a supportive and collaborative work environment that prioritises employee well-being and professional growth. With the flexibility of hybrid working and generous leave entitlements, including 36 days off, staff are encouraged to maintain a healthy work-life balance while contributing to the academic success of students in the Faculty of History.

Technical University of Denmark

Contact Details:

Technical University of Denmark Recruitment Team

We think you need these skills to ace AI-Driven Ultrasonic Materials PhD (Fully Funded) in Brighton

AI and Machine Learning
Ultrasonic Evaluation
Inverse Modelling
Neural Network Design
Large-Scale Simulations
Uncertainty Quantification
Model Validation