PhD Position in Maritime Studies

Supervisor:

• Tenured Associate Professor Serkan Turkmen,Estonian Maritime Academy: Estonian Maritime Academy: Green Maritime Technology Research Group

Proposed Thesis: Towards net zero in Maritime Industry: Enhancing full-scale power-speed assessment reliability by using IoT and big data management

Summary

This PhD research focuses on uncertainty challenges caused by various disturbances (wind, wave, current, biofouling, human factor) in full-scale ship speed-power measurements and analysis by testing various in-situ applications such Energy Saving Devices. The collected data could be used to build a digital twin. The aim of this PhD project is to demonstrate clean maritime systems performance and their scalability on a vessel. The PhD candidate will address these challenges by setting up performance monitoring systems using cost effective sensors and IoT devices. To enhance assessment reliability, full-scale measurements will be conducted. The results will be analysed to identify the cause of the disturbance. Empirical evidence will be supported by systematic literature reviews and disseminated in peer-reviewed journals. The candidates are expected to have an engineering degree, preferably in marine engineering and/or naval architecture ideally with an experimental and/or industrial background. Knowledge of ship system and marine hydrodynamics is essential.

Description

The research

Green technological solutions (e.g., renewable energy sources, Energy Saving Devices) have been proposed to mitigate ship generated Greenhouse Gas (GHGs) emissions. To maximise and sustain the performance of such solutions, big data collected from on-board and off-board sensors have been collected and analysed. However, such raw data collected from multiple sensors is often disturbed and does not indicate the ship performance at calm water conditions.

Practical methods and guidelines, such as ISO 19030 and ITTC recommended procedures and guidelines, are used to calculate actual speed-power performance by removing the influences (normalisation process). These empirical, computational and experimental methods and their reliability are often questioned due to various disturbances (wind, wave, current, biofouling, human factor) which can increase the uncertainty in the data and not easily normalised. This PhD research focuses on uncertainty challenges in full-scale ship speed-power measurements and analysis by testing various in-situ green ship technologies employing IoT.

Responsibilities and tasks

• Organising and conducting full-scale sea trials data collection using the Internet of Things, sensors and NMEA 2000
• Analysing the speed-power performance data using recommended guidelines and machine learning tools
• Defining the uncertainty sources
• Enhancing existing guidelines for full-scale power-speed assessment practice
• Disseminating research findings in written and oral form at conferences and in publications
• Engaging in interdisciplinary collaboration within Taltech and external research partners

Applicants should fulfil the following requirements:

• The candidates are expected to have a Master-level engineering degree, preferably in marine engineering and/ or naval architecture
• Knowledge of ship system and marine hydrodynamics is essential.
• Very good English skills (written, oral, academic)
• Strong and demonstrable writing and analytical skills and willingness to enhance these
• Strong motivation to work both independently and collaboratively with local and international stakeholders
• Willingness to engage in organizational tasks relevant to the project

The following experience is beneficial:

• An experimental and/or industrial background.
• Programming in at least one of the computer languages
• Knowledge of IoT
• Working on board ships

The candidate is expected to submit a research plan to show their knowledge of ship speed-power performance. The plan should include a data collection strategy using the Internet of Things, sensors and NMEA 2000, a data analyzing plan using established guidelines and machine learning tools.

We offer:

• A fully funded PhD position for four years in one of the largest and most internationalized research centers in Estonia
• A research environment with state-of-the-art computational resources, library and a university research vessel
• Opportunities for attending conferences, research visits and collaboration with other disciplines and institutes

Starting date: 1st of April 2026 / negotiable

The application should include:

The information for the PhD admission is available at TalTech´s web-page: https://taltech.ee/en/phd-admission

• A motivation letter describing your research interests
• Curriculum Vitae (CV)
• Degree certificates asrequired by the university
• Copy of Passport or ID document
• Contact information for references

Top candidates who enter the second round of recruitment will be notified and invited for interviews.

Send your application to:Prof Serkan Turkmen [emailprotected]