PhD Student in Wood Science and Engineering

Luleå University of Technology is in strong growth with world-leading competence in several research areas. We shape the future through innovative education and ground-breaking research results, and based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies, public actors and leading universities. Luleå University of Technology has a total turnover of SEK 1.9 billion per year. We currently have 1,840 employees and 17,670 students.

In the coming years, multi-billion investments will be made in large projects in Northern Sweden to create a fossil-free society both nationally and globally. Luleå University of Technology is involved in several of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope that you will help us to build the sustainable companies and societies of the future.We are looking for a motivated doctoral student for the research subject Wood Science and Engineering at Luleå University of Technology (LTU) who can carry out qualified research in our research group, both independently and in collaboration with colleagues. You will work within the strategic research program CT WOOD Centre of Excellence, which focuses research activities on X-ray computed tomography (CT) on wood. Most of the research within CT WOOD is conducted at the department of Wood Science and Engineering in Skellefteå. CT WOOD receives funding from The Swedish Forest Industry Federation (Swedish Wood), Kempe Foundations, Skellefteå Municipality and LTU, and several companies from the wood industry (Homepage). Additional funding for this position is provided by the Swedish Energy Agency (Energimyndigheten).

Subject description
Wood Science and Engineering includes an interdisciplinary approach on the wood material and industrial processes covering the value chain from forest to wood products. Research areas are anatomy, physics, chemistry, and mechanics related to wood, as well as wood processing and products engineering, manufacturing technology, process optimization and visualization for wood-based products.

Project description
The main objective for this research position is to implement machine learning methods as part of a greater artificial intelligence (AI) framework for the sawmill industry. A key task is to increase the level of integration of the data flow in the sawmill process using X-ray CT (computed tomography) technology. The AI framework should ultimately lead to a digital twin of a cutting-edge sawmill process for prediction and optimization of the product output. The data you are intended to work with are primarily derived from CT scans of timber (logs and boards), which will mainly be collected at our CT lab in Skellefteå. 

Duties
The research area is multidisciplinary in nature and includes areas such as material science, physics, data science, image analysis, and production technology. The work comprises the development of AI and deep learning models that make it possible to extract comprehensive and quality-relevant information from CT data of wooden logs and boards in an automated manner.

All scientific work is presented in English in the form of peer-reviewed articles in scientific journals and at international conferences, which are the main evaluation criteria to complete the PhD studies. In addition, 25% of your PhD studies are dedicated to course work, of which the majority can be chosen according to your needs and interests. At the end of your studies, you will summarize your work in a PhD thesis and defend it in front of a scientific committee.

Qualifications
Basic qualifications for postgraduate education have those who have: 1) completed a degree at advanced level 2) completed course requirements of at least 240 higher education credits, of which at least 60 higher education credits at advanced level, or 3) in some other way within or outside the country acquired mainly corresponding knowledge.

A suitable background is a university degree or otherwise certified expertise in computer science, data science, or a similar field. The applicant must have a clear interest in modelling, data analysis, experimental studies, and customary engineering issues. While not necessary, expertise in machine learning/deep learning, and the Python programming language (incl. PyTorch or Tensorflow/Keras) is appreciated. Even knowledge of finite element analysis and similar modelling approaches are of advantage. Knowledge of wood or material science is not necessary, but a merit if present.

The applicant must be a good team player with good knowledge of English, in both oral and written presentation. Knowledge of the Swedish language is not a requirement for the position, but the applicant should be able to perform tasks that require a basic understanding of Swedish in a few years' time.

For further information about the subject see; General curricula for the Board of the faculty of science and technology.


Further information
Employment as a PhD student is limited to 4 years, teaching and other department duties may be added with max 20%.

For further information about the position, please contact: Professor Dick Sandberg, +46 910-58 53 71, dick.sandberg@ltu.se och Benedikt Neyses, +46 72-219 69 79, benedikt.neyses@ltu.se.

Union representatives: SACO-S Kjell Johansson (+46)920-49 1529 kjell.johansson@ltu.se och OFR-S Lars Frisk, 0920-49 1792 lars.frisk@ltu.se.

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

See full job ad here, Open positions - Luleå University of Technology (ltu.se). 

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

Reference number: 3213-2023
Last day of application: 30 september 2023