Doctoral student in Computer Science, specialisation in evolvable AI

Description

The Faculty of Health, Science and Technology invites applications for a doctoral studentship in Computer Science at the Department of Mathematics and Computer Science.

As a doctoral student you will contribute to important knowledge for society and be part of the university’s significant investment in research education.

In the advertised position, you are expected to conduct research on the use of Artificial Intelligence (AI) in the context of predictive maintenance for industrial production systems. More specifically, you will investigate the question of how to support a systematic and smooth introduction of machine learning-based solutions for predictive maintenance as well as their continuous evolution during the operation of the production system. A particular focus in this regard is to address the lack of suitable training data which is very common when companies are about to introduce AI in their operations.

With this position, you have the opportunity to develop your creative and innovative ideas in the described research context, experiment with them, and evaluate them in a real-life context. The work will be conducted as part of an industrial research collaboration with one of the world’s largest producers of wood-based materials. Further collaboration opportunities may arise through synergies with other activities at Karlstad University, such as the interdisciplinary research center “Research Center for Digital Adaptive Manufacturing for Industry (DAMI)”.

As a doctoral student in the advertised position, you will be a member of the “Software Quality and Digital Modernization (SQuaD)” research group at the Department of Mathematics and Computer Science, which is a vibrant, inspiring, and collaborative research environment.

Duties

As a doctoral student, you will primarily dedicate time to your own studies. The doctoral programme, comprising of 240 credits for a Degree of Doctor and 120 credits for the licentiate degree is an opportunity to study while being paid a salary in combination with work and provides a stepping stone for a career in academia. The doctoral student is expected to work independently and be prepared to contribute to the activities and development of the subject and the department. Teaching and administrative duties may also be included upon agreement. Such work may cover a maximum of 20 % of full working hours. Work as a doctoral student allows for great freedom and flexibility in terms of how you structure your day. It takes commitment, discipline and a structured approach in order to successfully complete a doctoral thesis. Experienced supervisors offer guidance and support along the way. To contribute to a positive work environment and further the department’s activities, we expect you to be a present and active part of the day-to-day operations and workplace community

Qualification requirements

To be eligible for a doctoral studentship in computer science at Karlstads university, the applicant must meet the general and specific entry requirements (Higher Education Ordinance, Chap. 7, Sect. 35).

To meet the general entry requirements, the applicant must have been awarded a second-cycle qualification, satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second-cycle, or acquired substantially equivalent knowledge in some other way in Sweden or abroad (Higher Education Ordinance, Chap. 7, Sect. 39).

To meet the specific entry requirements for third-cycle studies in Computer Science, the applicant must have completed a Master’s degree (60 credits) in Computer Science, a Master’s degree (120 credits) in Computer Science or a Master of Science degree in Computer Engineering. A person who, in some other way, in the country or abroad, has acquired equivalent qualifications also meets the specific entry requirements.

Admission and assessment criteria

To be eligible for doctoral studies, the applicant must be considered to have the ability required to benefit from the programme (Higher Education Ordinance, Chap. 7, Sect. 35). Admission is based on individual assessment.

The assessment will focus on the applicant’s performance in previous studies and the quality of any independent projects completed during these studies. The most important assessment criteria for the selection are scientific skills, language skills in English, and suitability for conducting research in applied artificial intelligence for predictive maintenance.

Special weight will be given to the applicant’s background in artificial intelligence, in particular their knowledge of and experience with techniques for predictive maintenance, e.g., anomaly detection and classification, degradation modelling, or remaining-useful-life estimations. Moreover, knowledge and skills related to probabilistic modelling and synthetic data as well as experience with MLOps platforms will be given special weight. Solid data scientific and statistical skills as well as good knowledge of software engineering concepts and good programming skills are expected.

Considerable weight will be given to personal qualities such as the ability to cooperate, sound judgement, taking responsibility, commitment and a positive attitude towards colleagues and students, and the intention to be a present and active part of the research environment.

Terms

Upon admission to doctoral studies, the person will be offered the appointment of doctoral student (Higher Education Ordinance, Chap. 5, Sect. 3). The position comprises four years of full-time studies, or five years at a study rate of 80 % with 20 % teaching or other departmental duties. Other % distribution of studies and teaching or other institutional duties may also be considered. The position is fixed-term — one year initially, followed by possible extensions by a maximum of two years at a time. Start date by agreement.

The salary for the doctoral studentship corresponds to the standard level of salary for doctoral students and progresses in accordance with a local collective agreement.

The admission and employment are subject to necessary decisions.

Application

Submit the application via the University’s web-based recruitment tool, Varbi.

Applicants are responsible for submitting a complete application in accordance with the advertisement, for providing translations of any documents written in a language other than Swedish, Norwegian, Danish or English, and for ensuring that the documentation allows for objective and qualitative assessments. A complete application should be submitted by the application deadline. An incomplete application may jeopardise a fair assessment of qualifications.

The application should include:

• a cover letter describing the background of your interest in the position and outlining your research interests and skills related to the position,
• a résumé listing relevant qualifications (CV),
• degree certificate(s) with a complete transcript of the courses included, or a certified list of completed courses with grades and dates,
• copy of Master’s thesis or equivalent,
• if applicable, copies of publications (max. 3) or certificates for other qualifications,
• any letters of recommendation,
• Contact information of two references, such as a teacher, supervisor, or previous or current superior.

Attach all the documents and publications you wish to be considered to the electronic application (do not just provide links). Name each uploaded document to clearly indicate its content.

Documents that cannot be submitted electronically should be sent to the following address:

Karlstads universitet
HR-administrator
651 88 KARLSTAD

Application deadline: November 17, 2024

State the reference number: REK 2024/187

Applicants with qualifications from abroad

If you have qualifications from a university outside Sweden or if they have not been recognised by a Swedish authority before, it is important that you attach degree certificates and official transcripts to facilitate the recognition of foreign qualifications.
Provide a contact for the degree-awarding institution (phone number or email) who can verify your qualifications.

We look forward to your application!

Karlstad University has chosen advertising channels for this recruitment and firmly declines any contact with advertising or recruitment agencies.