Doctoral student in Computer Science - Hiring in process/Finished, not possible to apply

Description

Are you interested in the interaction between machine learning, management of complex energy systems and Edge/Cloud Compute?

The Faculty of Health, Science and Technology is accepting applications for a doctoral studentship leading to a PhD in Computer Science with focus on application of machine learning in smart electricity grids for large scale PV integration at the Department of Mathematics and Computer Science.

Within your thesis project, you will develop Machine Learning based methods to improve forecast accuracy for energy supply from photovoltaic systems and predict energy demand and explore the impact of imperfect data on grid energy forecasting and its uncertainty. For integration of such forecasting algorithms into real systems, you will work towards drift detection and automatic model re-training techniques for developing more effective solutions for electricity grid real-time operations and planning. Finally, you will integrate your algorithms into IoT based architectures such as Edge Compute in order to limit grid investments for grid reinforcement and data collection sites.

The doctoral position is part of a multi-disciplinary research team, and embedded into the AI4ENERGY project funded by Energimyndigheten and the Solar Electricity Research Centre Sweden, SOLVE. For this position, you will collaborate with researchers and doctoral students at both KAU, Dalarna University, Mälardalen University, the Swedish University of Agricultural Sciences, RISE and Uppsala University.

Duties

A doctoral student is mainly expected to engage in doctoral studies, which includes participation in research projects and postgraduate courses. The doctoral program comprises 240 higher education credits, including the doctoral thesis and 120 higher education credits for a Degree of Licentiate.

The doctoral student is going to research, how machine learning and edge/cloud computing can result in better management and control of smart grids. The goal of the doctoral project is to design and implement data driven models and use them for making predictions connected to smart grid  management.

Qualification requirements

To be eligible for doctoral studies, 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 second-cycle qualifications, completed at least 240 higher education credits, including at least 60 credits at master level, or who have acquired largely equivalent knowledge in some other way in Sweden or abroad. (Higher Education Ordinance, Chap. 7, Sect. 39)

A person with 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 technology has special eligibility for admission to doctoral studies in computer science. A person who in some other way in the country or abroad has acquired equivalent qualifications also has special eligibility.

Admission and assessment criteria

To be eligible for doctoral studies, the applicant must be deemed to have the ability required to pursue the program successfully (Higher Education Ordinance, Chap. 7, sect. 35). Admission is based on individual assessment.

Selection is based on previous study results. Special weight will be given to the quality of independent research or investigative projects executed during previous studies, and in particular to such projects completed at a master level and in the proposed research specialization. The most important assessment criteria for the selection are scientific skills, suitability for the project, and the documented ability to both work in groups and independently carry out tasks. Weight will be given to documented knowledge of and background in applied machine learning. Experience in developing algorithms for machine learning and their practical implementation in edge computing platforms is an advantage.

For further information on research and the doctoral program in Computer Science at Karlstad University, see www.kau.se/cs

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. The position is fixed-term — one year initially, followed by possible extensions by a maximum of two years at a time. Start date as negotiated.

The salary for the doctoral studentship corresponds to the standard level of salary for doctoral students at Karlstad University.

Further information

Karlstad University places great value on the enriching presence of diverse backgrounds and gender balance in the organization. We welcome applicants with different cultural backgrounds, gender identities, functional abilities, and life experiences.

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, that any documents in languages other than Swedish and English have been translated 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 jeopardize a fair assessment of qualifications.

The application must include the following items:

• a cover letter briefly describing the reasons why you are interested in the position and why do you think your qualifications and skills fit to the duties of the position (max. one page),
• CV
• degree certificate with a complete transcript of the courses included, or a certified list of completed courses with grades and dates,
• copy of Master’s thesis in computer science or equivalent work, and copies of any relevant scholarly publications,
• contact details of two references, at least one of whom should be a current or previous supervisor, or equivalent.
• 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:

Karlstads universitet
Josefin Rönnqvist
651 88 KARLSTAD

Application deadline: 20-06-2022

State the ref.no: REK 2022/104.

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.