PhDInformatics Engineering
| Study location | Lithuania, Vilnius, On Campus |
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| Academic field | Computer science (ISCED 481) Information systems (JACS I200) |
| Type | Doctoral, full-time |
| Nominal duration | 4 years (30 ECTS) |
| Study language | English |
| Awards | PhD (Artificial Intelligence Datasets Quality and Value) |
| Course code | Informatics Engineering T 007 |
| Application fee | €100 one-time |
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| 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
Artificial Intelligence Datasets Quality and Value
Open application for a doctoral candidate in Informatics Engineering
The doctoral candidate position is available in the Faculty of Fundamental Sciences, Department of Information Technology. Applicants interested in other research topics related to Computer Science are also welcome to apply.
Research topic description
The application of artificial intelligence, and smart solutions, is becoming one of the tools to increase the data analysis potential and gain advances against concurrent. The current level of development of artificial intelligence methods and infrastructure is no longer a constraint. The biggest obstacle to faster and more value-added artificial intelligence methods is the lack of data for training in artificial intelligence methods.
The dataset for training in artificial intelligence techniques is one of the key determinants of the quality of artificial intelligence. It must be large enough to ensure the accuracy of the data, match the pattern of the actual data, and the wide distribution of the values of the properties must be assured. Applying the wrong dataset can result in the inaccurate performance of the model, or (even worse) results of the accuracy are misleading as the model works with test data but is unable to adapt to new data under real-world conditions. It is necessary to ensure greater availability and quality of datasets. At present, the quality of the data set is assessed by experts rather than automatically. Scientific publications are still more concerned with determining the value of a dataset than its quality, which often reflects the popularity and need for the dataset rather than the quality and sufficiency of the data itself.
The main goal of this dissertation is to create a dataset quality determination method to automate the suitability and value of the dataset for the task of artificial intelligence.
Preliminary tasks are set for the implementation of the goal:
1. To get acquainted with the methods of dataset development and quality management.
2. To design a taxonomy of the factors influencing the quality of the dataset and the methods for evaluating them.
3. To propose a model for determining the quality of a dataset, expressing it quantitatively.
4. To evaluate the suitability of the developed model, evaluating the similarity of the quality of different datasets the results between the proposed model and obtained during the expert evaluation.
The development of an automated dataset quality assessment model will allow it to be used not only to simplify the application of artificial intelligence techniques but also to control the development of datasets to assess whether existing data are sufficient for application and lack to improve their quality.
The selected candidate will work on the PhD thesis under the supervision of Prof. dr. Simona Ramanauskaitė. The successful applicant will have to attend scientific conferences, meetings and internships at other universities. .
Requirements
• Required background: Master’s degree in Computer Science or a related area.
• Expected skills and knowledge: Systemic literature analysis, research methodology, artificial intelligence knowledge and skills.
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. Simona Ramanauskaitė, e-mail: simona.ramanauskaite@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.