PhD Researcher: Torque Estimation for Maritime Powertrains

Aalto University; Finland; On-site; EUR 29,000 - 35,000; Full time

Benefits offered by this job

Occupational health care

Job summary

Aalto University Rotor Lab at the Department of Mechanical Engineering is seeking a Doctoral Researcher / Master’s Thesis Worker for VIcTOR: Virtual Torque Sensing for Maritime Powertrains. You will develop torque estimation methods and verify them with Wärtsilä data, aiming for embedded deployment and Simulink-based SIL/MIL testing.

The project combines convex optimization and physics-informed learning, using Matlab/Simulink, Python, and embedded platforms.

Qualifications

• Master's or Bachelor's degree in mechanical engineering or related field.
• Excellent written and oral skills in English.
• Finnish language skills are a benefit.

Responsibilities

• Define the accuracy and requirements for the torque estimation algorithm.
• Build and verify a torsional vibration model with Wärtsilä data.
• Develop convex optimization-based and physics-informed learning approaches for torque estimation.
• Study the framework with simulated measurements first, then verify with Wärtsilä measurements.
• Refine the model for embedded implementation with limited computing capacity.

Skills

Mechanical engineering

Control engineering

Applied mathematics

Electrical engineering

Torsional vibration

Rotordynamics

Powertrain dynamics

State estimation

Convex optimization

Kalman filtering

Physics-informed learning

Matlab/Simulink

Python

C / embedded languages

Embedded systems

Education

Bachelor's or Master's in mechanical engineering

Bachelor's or Master's in relevant field

Tools

Matlab/Simulink

Python

C

Embedded-systems languages

Job description

Aalto University Rotor Lab at the Department of Mechanical Engineering is seeking a Doctoral Researcher / Master’s Thesis Worker for VIcTOR: Virtual Torque Sensing for Maritime Powertrains. You will develop torque estimation methods and verify them with Wärtsilä data, aiming for embedded deployment and Simulink-based SIL/MIL testing.

The project combines convex optimization and physics-informed learning, using Matlab/Simulink, Python, and embedded platforms.