PhD Position in AI-Driven Adaptive Robotic Manufacturing Systems
This PhD position at Tallinn University of Technology (TalTech) offers an exciting opportunity to conduct research in AI-driven adaptive robotic manufacturing systems for Industry 5.0. The project aims to develop intelligent, process-adaptable robotic systems capable of responding dynamically to changing production requirements, product variants, and uncertain operating conditions. The research integrates advanced concepts such as intelligent control, digital twins, and human–robot collaboration to accelerate the development and manufacturing of complex products.
Based at TalTech’s Virumaa College and embedded in the ÕÜF10 project, the doctoral research is supported by the Industry 5.0 Future Test Lab, which features collaborative and mobile robots, a humanoid robot, a quadruped robot, large-format additive manufacturing systems, high-speed and thermal imaging equipment, and a metrology-grade 3D scanner. These facilities provide a unique platform for developing and validating next-generation adaptive manufacturing technologies.
The research topic is intentionally broad, allowing candidates to shape their work according to their expertise in robotics, artificial intelligence, or digital manufacturing. Potential research directions include learning-based control, adaptive task and motion planning, machine perception, autonomous decision-making, and data-driven process optimisation. Digital twin technologies will be used to connect robotic systems with product and production lifecycle data, enabling simulation-driven design, virtual commissioning, and real-time process monitoring and optimisation. Human–robot collaboration, including safety, ergonomics, and user acceptance, forms an important part of the research.
Responsibilities include investigating and developing adaptive solutions for collaborative, mobile, and process-adaptable robots using AI/machine-learning methods; developing digital twin–driven methodologies; evaluating models for human–robot interaction and co-creation; integrating software and hardware solutions; using simulation and augmented/virtual environments for modelling and validation; applying collaborative and mobile robots, additive manufacturing, high-speed and thermal imaging, and 3D scanning for fabrication, process monitoring, and digital twin construction; planning and conducting experiments; analysing and interpreting data; contributing to laboratory development; publishing results; and collaborating with the ÕÜF10 project team and industrial partners.
Applicants must have a master’s degree (or equivalent) in Mechanical Engineering, Robotics, Mechatronics, Industrial Engineering, Computer Science, or a closely related field. Solid knowledge in at least one of robotics and automation, digital twins, modelling and simulation, AI/machine learning, manufacturing technologies and Industry 4.0/5.0, or human–robot interaction is required. Programming experience in Python, C++, or MATLAB is necessary. Familiarity with robotics and/or simulation software (e.g., ROS, Unity) is welcome but can be acquired during the studies. Candidates should understand experimental research methods and be able to work with both software and hardware systems. Ability to work independently and collaboratively in an international research environment, with good analytical skills, and excellent English language skills (written and spoken) are required. Beneficial experience includes collaborative robots, mobile manipulators, legged/humanoid robots, ROS/ROS 2, additive manufacturing, machine vision, high-speed imaging, thermal imaging, 3D scanning/metrology, user-centred design, ergonomics, safety in human–robot interaction, and previous research experience demonstrated through publications or thesis work.
The position offers a four-year doctoral contract at one of Estonia’s leading technical universities, involvement in the ÕÜF10 project, access to Virumaa College and Smart Industry Centre laboratories, and a collaborative, international research environment with opportunities for conference participation, exchanges, and industry cooperation. Supervision is provided by Full Professor Tauno Otto and Postdoctoral Researcher Kaleem Arshid, both from the School of Engineering. TalTech is a modern, international scientific community with strong ties to local and international industry, aiming to lead the way to a sustainable digital future.
Applications can be submitted online from 2 June 2026 to 30 June 2026. For more information about the admission process, visit the PhD Admission homepage. Prepare all required documents and ensure eligibility before applying.
