PhD in ML & Signal Processing for Industrial Flow Analysis

EURAXESS Czech Republic; Trondheim; Hybrid; NOK 496,000 - 606,000; Full time

Benefits offered by this job

Free Norwegian language training (A2)

Job summary

NTNU Norwegian University of Science and Technology seeks a motivated PhD Candidate to join the LAIFA project in Trondheim. You will work toward a doctoral degree while gaining experience in higher education and research, with ties to the BRU21 program and IoT@NTNU consortium.

The position is a 3-year doctoral contract (4 years with 25% teaching/ duty work). You will contribute to signal processing and machine learning research, aiming at reconstructing flow in various operating conditions and

Qualifications

• Master's degree in Electrical Engineering, Computer Science, Applied Mathematics, or other relevant disciplines with 120 credits at master's level.
• Strong mathematical background and a research-oriented master thesis in a related field (e.g., signal processing, machine learning).
• Significant programming experience (preferably Python).
• Fluent English in oral and written communication; English tests accepted (TOEFL/IELTS/CAE/CPE).

Responsibilities

• Complete the doctoral education and obtain a doctorate.
• Carry out high-quality research within the project scope (LAIFA).
• Publish academic papers and disseminate findings; participate in the Signal Processing group.
• Engage in international activities such as conferences and research stays.
• If on a 4-year contract, provide teaching assistance.

Skills

Python

Mathematics

English

Education

Master's degree in Electrical Engineering/Computer Science

Job description

NTNU Norwegian University of Science and Technology seeks a motivated PhD Candidate to join the LAIFA project in Trondheim. You will work toward a doctoral degree while gaining experience in higher education and research, with ties to the BRU21 program and IoT@NTNU consortium.

The position is a 3-year doctoral contract (4 years with 25% teaching/ duty work). You will contribute to signal processing and machine learning research, aiming at reconstructing flow in various operating conditions and