PhDMechanical Engineering
| Study location | Lithuania, Vilnius, On Campus |
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| Academic field | Building and civil engineering 06.4 (ISCED 582) Mechanical engineering (JACS H300) |
| Type | Doctoral, full-time |
| Nominal duration | 4 years (30 ECTS) |
| Study language | English |
| Awards | PhD (PhD candidate position in “Research and Development of Human Digital Twin”) |
| Course code | Mechanical engineering T009 |
| Tuition fee | €12,449 per year Part-time studies (6-year) 8 229,00 |
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| Application fee | €100 one-time |
| Entry qualification | Postgraduate diploma (or higher) The entry qualification documents are accepted in the following languages: English. Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original. You must take verified copies of the entry qualification documents along with you when you finally go to the university. |
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| Language requirements | English International applicants to whom English is not a native language need to provide a proof of their English language proficiency. Exceptions are made only for applicants who have completed their previous studies fully in English. One of the following is accepted: |
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| Other requirements | At least 2 reference(s) must be provided. A relevant portfolio is required. Please upload your research proposal including the abstract, literature review, research objectives, research questions, methodology and bibliography. - Certified copies of the Master’s degree diploma and its supplement with grades or higher education equivalent to it; |
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| More information |
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Overview
PhD candidate position in “Research and Development of Human Digital Twin”
Open application for a doctoral candidate in Mechanical Engineering
The doctoral candidate position is available Faculty of Mechanics, Department of Biomechanical Engineering. Applicants interested in other research topics related to Mechanical Engineering are also welcome to apply.
Research topic description
The rising demand for remote healthcare solutions and home-centred physical activity programs has led to a need for intelligent, adaptive, and personalized exercise monitoring systems. A digital twin, which is a nearly real-time digital replica of a physical entity, is primarily utilized to monitor, analyze, and enhance real objects in manufacturing and various technical fields to cut down costs associated with human resources and reduce risks [1]. Applying the digital twin concept introduces new opportunities for improving physical activity [2]. The digital twin functions as an all-encompassing diagnostic tool, enabling physicians to access a patient’s complete history, including metrics, interventions, and biological data via a virtual model. This enables more accurate selection and planning of treatment methods, with the ability to foresee outcomes [3-7]. This PhD research is focused on developing a Virtual Assistant based on Digital Twin technology to guide users through home exercises, ensuring optimal performance, preventing injuries, and maintaining long-term engagement. The Digital Twin (DT) methodology will generate a real-time biomechanical model of the user by integrating motion capture, biosignal monitoring, and AI-based analytics. This virtual model will provide personalized feedback, customized exercise recommendations, and real-time adjustments based on movement patterns, physiological responses, and potential risk factors. The research will involve creating a human digital twin multi-layered biomechanical user model, incorporating full-body motion capture and biosignal data, and real-time data processing systems for movement analysis and feedback generation. It will also involve implementing machine learning algorithms for recognizing gestures, classifying motions, and detecting anomalies in movement patterns, using wearable sensors to monitor biomechanical and physiological parameters, and applying interactive visualization techniques for intuitive feedback and real-time correction. This research will contribute to biomechanics, AI-driven digital health, rehabilitation engineering, and personalized healthcare technologies. The findings could lay the groundwork for innovative digital health solutions enhancing physical activity engagement and supporting healthy ageing, post-injury rehabilitation, and sports performance optimization.
1. ‘Industry 4.0 and the digital twin Manufacturing meets its match A Deloitte series on Industry 4.0, digital manufacturing enterprises, and digital supply networks’. Accessed: Feb. 08, 2019. [Online]. Available: www2.deloitte.com/content/dam/Deloitte/cn/Documents/cip/deloitte-cn-cip-industry-4-0-digital-twin-technology-en-171215.pdf
2. R. Gámez Díaz, Q. Yu, Y. Ding, F. Laamarti, and A. El Saddik, ‘Digital Twin Coaching for Physical Activities: A Survey’, Sensors, vol. 20, no. 20, p. 5936, Oct. 2020, doi: 10.3390/s20205936.
3. C. P. Benziger, M. D. Huffman, R. N. Sweis, and N. J. Stone, ‘The Telehealth Ten: A Guide for a Patient-Assisted Virtual Physical Examination’, Am J Med, vol. 134, no. 1, p. 48, Jan. 2021, doi: 10.1016/J.AMJMED.2020.06.015.
4. O. J. Mechanic, Y. Persaud, and A. B. Kimball, ‘Telehealth Systems’, StatPearls, Sep. 2021, Accessed: Nov. 28, 2021. [Online]. Available: www.ncbi.nlm.nih.gov/books/NBK459384/
5. D. Charles, D. Holmes, T. Charles, and S. McDonough, ‘Virtual Reality Design for Stroke Rehabilitation’, Adv Exp Med Biol, vol. 1235, pp. 53–87, 2020, doi: 10.1007/978-3-030-37639-0_4.
6. D. Mattia et al., ‘The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: A study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response’, BMC Neurol, vol. 20, no. 1, Jun. 2020, doi: 10.1186/s12883-020-01826-w.
7. P. Falkowski et al., ‘Study on the Applicability of Digital Twins for Home Remote Motor Rehabilitation’, Sensors, vol. 23, no. 2, p. 911, Jan. 2023, doi: 10.3390/s23020911.
The selected candidate will work on the PhD thesis under the supervision of Prof. Dr Julius Griškevičius. The successful applicant will have to attend scientific conferences, meetings and internships at the other universities.
Requirements
• Required background: Master’s degree in biomedical engineering, Biomechanics, Computer Science, Robotics, or a related field. A strong interdisciplinary background in motion analysis, AI, digital health, or rehabilitation technology is highly desirable.
• Expected skills and knowledge: experience in biomechanical modelling, motion capture, AI/machine learning, proficiency in programming, familiarity with sensor fusion and interactive visualization, problem-solving mindset are highly desirable.
It is a prerequisite you can be present at and accessible to the institution daily.
For more information
Shortlisted candidates will be invited for an interview. The position may not be opened if no qualified candidate is found. Additional information regarding the post may be obtained from Prof. Dr Julius Griškevičius e-mail: julius.griskevicius@vilniustech.lt
Programme structure
The PhD programme consists of:
· Independent research under supervision;
· Courses for PhD students (approximately 30 ECTS credits);
· Participation in research networks, including placements at other, primarily foreign, research institutions;
· Teaching or another form of knowledge dissemination, which is related to the PhD topic when possible;
· The completion of a PhD thesis.
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