TalTech invites applications for a funded PhD position in Cyberattack Detection Methods in Power Systems within the SIPSIK project (Securing Power Systems against Cyberattacks). The research combines cybersecurity, data mining and machine learning to develop interpretable and efficient methods for detecting cyberattacks against critical energy infrastructure. The candidate will join an interdisciplinary team working with academic and energy-sector partners.
The project will address the following key areas:
Modern power systems increasingly depend on interconnected digital devices, communication networks and automated control. While these technologies improve operational efficiency, they also introduce vulnerabilities through which cyberattacks can disrupt electricity generation, transmission and distribution.
This doctoral research will develop interpretable and computationally efficient cyberattack-detection methods for power-system environments. The work will investigate data mining, association-rule mining and machine learning to identify abnormal patterns and security-relevant relationships in operational data. Particular emphasis will be placed on transparent detection rules that help operators understand alerts and distinguish malicious activities from legitimate operational changes.
The candidate will investigate data representation, feature selection and detection algorithms, considering trade-offs between detection accuracy, false alarms, interpretability and computational cost. The transferability of the proposed methods across operating conditions and attack scenarios will also be examined.
The methods will be evaluated using real-world and simulated datasets, supported by controlled experiments involving network simulation and real-time digital simulation where appropriate. The research forms part of SIPSIK, Securing Power Systems against Cyberattacks, and aims to produce scientifically validated methods and practical recommendations for protecting critical energy infrastructure.
Responsibilities and (foreseen) tasks:
Review existing cyberattack-detection methods and identify research gaps.
Prepare and analyse operational and simulated power-system datasets.
Develop interpretable detection algorithms using data mining and machine learning.
Validate and benchmark algorithms under representative cyberattack scenarios.
Collaborate with project partners and communicate research findings.
Publish scientific results and prepare the doctoral thesis.
Applicants should fulfil the following requirements:
A master’s degree or equivalent qualification in computer engineering, cybersecurity, electrical engineering, power systems, computer science or a closely related discipline.
A strong background in at least one relevant area: data mining, machine learning, cybersecurity or power-system modelling and simulation.
Good programming skills, particularly in Python.
An understanding of computer networks, digital systems or power-system operation.
The ability to conduct independent research, analyse experimental results and communicate findings clearly.
Good written and spoken English, meeting TalTech’s doctoral admission requirements.
The ability to work independently and collaborate within an interdisciplinary research team.
The following experience is beneficial:
Association-rule mining, anomaly detection or interpretable machine learning.
Processing and analysing time-series, network-traffic or energy-sector datasets.
Smart grids, industrial control systems or power-system protection and automation.
Network simulation using NS-3, real-time digital simulation using RTDS, or comparable tools.
Cyberattack modelling, intrusion detection, threat analysis or cybersecurity monitoring.
Scientific writing, research projects or publications in relevant fields.
We offer:
A funded doctoral position with a gross monthly salary of at least EUR 2,300 for full-time employment.
An international and interdisciplinary research environment at TalTech.
Supervision combining expertise in cybersecurity, computing and power systems.
Collaboration with academic researchers and energy-sector partners through the SIPSIK project.
Opportunities to develop and validate research using operational data and simulation environments.
Opportunities to publish research and present findings at scientific conferences.
Tuition-free doctoral studies and opportunities for professional development.
Supervisors:
Main supervisor: Assistant Professor Tara Ghasempouri
Co-Supervisor: Relay protection and Automation Expert Marko Tealane, School of Engineering: Department of Electrical Power Engineering and Mechatronics
Co-Supervisor: Director Mart Landsberg, School of Engineering: Department of Electrical Power Engineering and Mechatronics
Tallinn University of Technology (TalTech) is an international scientific community with approximately 9,000 students and 2,000 employees; it is one of the largest universities in Estonia, the leading EU country in digitalisation. The university's strengths are broad multidisciplinary study/research interests, a modern research environment, and strong collaboration with international educational and research institutions. TalTech is aiming to be an organisation leading the way to a sustainable digital future.
The Department of Computer Systems is part of the School of Information Technologies at Tallinn University of Technology (TalTech). Its research focuses on secure, dependable and intelligent computing systems. Areas of expertise include the reliability, verification and testing of nanoelectronic systems, intelligent and control systems, virtual reality and biorobotics. The department provides education at bachelor’s, master’s and doctoral levels and contributes to the Computer and Systems Engineering curricula. Through international research collaboration and interdisciplinary projects, it connects computing research with practical technological challenges
For information about the admission process, please visit the PhD Admission homepage or contact the doctoral admission team at docstudy@taltech.ee.
Applications can be submitted from 04.11.2026 to 04.12.2026
For further info, please contact:
Tara Ghasempouri, Email: tara.ghasempouri@taltech.ee
Marko Tealane, Email: marko.tealane@taltech.ee
Mart Landsberg, Email: mart.landsberg@taltech.ee
