PhDElectrical and Electronics Engineering
| Study location | Lithuania, Vilnius, On Campus |
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| Academic field | Electricity and energy 06.2 (ISCED 522) Electronic & electrical engineering (JACS H600) |
| Type | Doctoral, full-time |
| Nominal duration | 4 years (30 ECTS) |
| Study language | English |
| Awards | PhD (PhD candidate position in Design Methods of Low-Power Computer Vision Using Deep Learning) |
| Course code | Electrical and Electronic Engineering T001 |
| Tuition fee | €11,589 per year Part-time studies (6-year) 7 726,00 |
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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. The applicant is required to submit a portfolio - 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 the Design Methods of Low-Power Computer Vision Using Deep Learning
Open application for a doctoral candidate in Electrical and Electronic Engineering
The doctoral candidate position is available in the Electronics, Department of Electronic Systems faculty.
Research topic description
To enable IoT systems to see and understand the environment, edge-based machine vision would come as a solution. Edge computing allows for data processing and analysis to be performed closer to the source of data, reducing the time it takes for data to be transmitted to a remote server. This can be particularly beneficial in applications where low latency is crucial, such as real-time monitoring and control. Edge computing also allows for reduced bandwidth, improved security, increased scalability and reliability, and reduced cost of IoT systems. However, several challenges need to be overcome to perform computer vision on the edge for IoT applications: limited computational resources, power constraints, and limited storage. Specific design methods are required for low-power computer vision, which is based on deep learning algorithms.
The selected candidate will work on the PhD thesis under the supervision of prof. dr. Dalius Matuzevičius. The successful applicant will have to attend scientific conferences, meetings, and internships at other universities.
Requirements
• Required background: math, statistics, engineering
• Expected skills and knowledge: computer vision, machine learning, linear algebra, python programming, deep learning frameworks
It is a prerequisite for being 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. Dalius Matuzevičius, e-mail: dalius.matuzevicius@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.