PhD Student Position in Machine Learning

Luleå University of Technology is experiencing strong growth with world-leading competence in several areas of research. Our research is conducted in close collaboration with industries such as LKAB, Ericsson, Boliden, ABB, Epiroc and leading international universities. Luleå University of Technology has a total turnover of SEK 1.8 billion per year. We currently have 1 770 employees and 17 200 students.

We shape the future through innovative education and groundbreaking research. Drawing on our location in the Arctic region, we create global societal benefit.


The Machine Learning Research Laboratory has an open position in the area of Machine Learning. We offer well-equipped laboratory facilities for performing research and good academic network in Sweden and abroad.

Subject Description

Machine learning focuses on computational methods by which computer systems uses data to improve their own performance, understanding, and to make accurate predictions and has a close connection to applications.

Project Description

As PhD you will be part of a project aiming at the exploration of Machine Learning methods for rock classification from the Drill Core raw data until the material classification. It covers method in preprocessing of the data, analyzing different data sources, combining those sources, and developing an adjustable end-to-end system for drill core analysis. You will collaborate actively with Boliden Mineral during ongoing work with developing systems to assist geologists in their interpretation of drill cores. This will involve several visits to Boliden’s operations in Europe. You will form part of a larger project group consisting of staff from the divisions of Machine Learning and Ore Geology at Luleå University of Technology, and experts from Boliden Mineral’s department of Exploration technology.

You will be based at the Division of Embedded Internet Systems Lab (EISLAB) Machine Learning group and will be jointly supervised by senior staff from the Ore Geology division at Luleå University of Technology. The project is funded by the Boliden Mineral’s departments of Exploration Technology and is based in Luleå. You will have a tight collaboration with a PhD student from Ore Geology on automatic rock classification.

Here you can find more information about the PhD: curricula for the Board of the faculty of science and technology.


We seek a highly motivated and enthusiastic and PhD student with a MSc degree in Computer Science, preferably Machine Learning. Experience in Deep Learning Framework or industrial experience is required. Documented hands-on experience with applying machine learning models to datasets generated by modern core-scanning technology is considered as an advantage. You must have good knowledge of English both in speech and writing and have the capacity to work independently as well as in teams.


PhD students will be offered a position for 4 to maximum 5 years (if teaching or other departmental work is included in the position). Entrance to the position: enrolment starting as soon as possible or by agreement. Location: Luleå.

For further information, please contact: Professor Marcus Liwicki, (+46)920-49 1006

Unions Representatives: SACO-S Kjell Johansson, (+46)920-49 1529 OFR-S Lars Frisk, (+46)920-49 1792

Luleå University of Technology is actively working on equality and diversity that contributes to a creative study- and work environment. The University’s core values are based on respect, openness, cooperation, trust and responsibility.

In case of different interpretations of the English and Swedish versions of this announcement, the Swedish version takes precedence.


We prefer that you apply for this position by clicking on the apply button below. The application should include a CV, personal letter and a copy of verified diploma from university. Your application, including diplomas, must be written in English or Swedish. Mark your application with the reference number below.

Final day to apply: 21 July 2021

Reference number: 2282-2021