A doctoral (PhD) student in Additive Manufacturing and Machine Learning

Department of Engineering, Mathematics and Science Education

We conduct research and education in physics, chemical engineering, mechanical engineering, mathematics, industrial design engineering and didactics in mathematics and science education. We offer unique and world-leading research within the FSCN Research Centre and Sports Tech Research Centre.

Job description
We are seeking a highly motivated PhD student to join our research group in an interdisciplinary project bridging additive manufacturing (AM) and artificial intelligence, machine learning and deep learning. This position is part of the NEXRAM initiative, aimed at addressing industrial challenges through close collaboration with leading industry partners. The project is a part of a national initiative funded by the Knowledge Foundation, offering opportunities for collaboration within a nationwide network of PhD students and researchers across various disciplines.

The research will focus on optimizing additive manufacturing of refractory materials using powder bed fusion with electron beam technology. This involves integrating advanced sensors (e.g., backscatter detectors, optical imaging, and pyrometry), developing machine learning models, and implementing real-time process feedback mechanisms to improve material quality, reliability, and sustainability in metal additive manufacturing. The research will be applied and multidisciplinary, including both laboratory work, modelling, programming, and material characterisation. You will have access to state-of-the-art facilities and collaboration with leading experts in mechanical engineering, materials science, and data science.

Independent third-cycle studies encompassing research (80-100%), teaching and administrative tasks (0-20%).

Entry requirements
General entry requirements include a second-cycle level qualification, or fulfilment of courses comprising at least 240 credits of which at least 60 credits must be at second-cycle level, or the equivalent knowledge gained in some other way in Sweden or abroad. Specific entry requirements include at least 90 credits in the subject of Engineering Physics/Physics/Materials Science/Mechanical Engineering or the equivalent knowledge gained in some other way in Sweden or abroad.

Assessment criteria
Previous experience in additive manufacturing and machine learning, artificial intelligence or deep learning are advantageous. Experience in research projects and teaching is also advantageous.

Personal qualities
We are looking for an individual who is initiative-driven, independent, performance-oriented, collaborates effectively with others, and demonstrates strong organization and structure.

Other assessment criteria
In addition to the formal eligibility requirements, the selection will also be based on other work (such as thesis work and project work), previous employment, and interviews with applicants.

The research is conducted in collaboration with national industry but also in an international environment. Therefore, very good skills in English, both oral and written, are advantageous.

A description of the applicant's vision and goals regarding the field of additive manufacturing and machine learning should be included in the application.

Employment process
Processing of the appointment will comply with the provisions in Chapter 5 of the Higher Education Ordinance, and will be carried out in accordance with Mid Sweden University’s Employment Procedures. 

Terms of employment
The doctoral studentship position corresponds to four years of full-time studies and is expected to lead to a Degree of Doctor. The entry date is 01-04-2025 or as agreed.

Initial doctoral student positions are valid for a maximum of one year, after which the position may be renewed by no longer than two years at a time. Provisions regarding doctoral studentship employment can be found in the Higher Education Ordinance (1993:100), Chapter 5, Sections 1-7.

Place of employment: Östersund

Salary: In accordance with Mid Sweden University’s salary scale for doctoral students.  

Information
For more detailed information contact: Prof. Lars-Erik Rännar, lars-erik.rannar@miun.se, +46 10 1428417; or visit the department online at:
https://www.miun.se/en/meet-mid-sweden-university/Organisation/departments/imd/,
or  Sports Tech Research Centre at:
https://www.miun.se/en/Research/research-centers/sports-tech-research-centre/

Prof. Jan Lundgren, jan.lundgren@miun.se, +46 10 1428556; or visit the research centre Sensible Things that Communicate at: https://www.miun.se/en/Research/research-centers/stc/ 

Application
Application documents must be submitten in Swedish or English.

The application should include a verified CV, copies of official transcripts and degree certificates, description of objectives according to the above as well as other documents the applicant would like to refer to.

Please send in your application through our recruitment system no later than 06-01-2025. 

Mid Sweden University has two campuses; in Sundsvall and in Östersund, both located close to students, colleagues and the surrounding society. At the same time, it is located conveniently close to the sea, forests and mountains, which provides a great quality of life. Here, new knowledge is created by means of internationally successful research and education that contribute to the development of our society. This way, we actively contribute to a sustainable future and a better world. Welcome to a university where people meet, get inspired and think in new ways.  Mid Sweden University works actively for equal opportunities and strives to embrace the qualities that diversity and equality bring to the organization. Prior to any recruitment work, Mid Sweden University has taken a position on consulting support, recruitment channels and marketing. We therefore kindly ask media vendors, recruitment sites and the like not to contact us.

Contract type

Full time

First day of employment

Enligt ök

Salary

Enligt Mittuniversitetets lönetrappa för doktorander

Number of positions

1

Working hours

100%

City

Östersund

County

Jämtlands län

Country

Sweden

Reference number

MIUN 2024/3184

Union representative

Per Bergman, Fackförbundet ST, 010-1428371

Börje Norlin, Saco, 010-1428594

Published

2024-12-04

Last application date

2025-01-06