Researcher, machine learning

“Our School and research environment is in a very exciting and expansive phase.

It is a large research environment with a strong international character. The goal is to be a nationally leading environment within, among other things, applied AI” – Magnus Clarin, Dean of the School of Information Technology.

Halmstad University

Halmstad University prepares people for the future by creating values, driving innovation and developing society. Since the beginning in 1983, the University has been characterised as forward-thinking and cross-border. Today, the University has around 600 employees and 11,000 students. The range of education is wide and the research is internationally renowned. Halmstad University actively participates in social development through collaboration with both industry and the public sector. More information about working at Halmstad University: https://hh.se/english/about-the-university/vacant-positions.html

Halmstad University consists of four interdisciplinary Schools and the current position is located at the School of Information Technology (ITE). .

More information about the School of Information Technology: https://hh.varbi.com/center/tool/position/390021/edit/tab:1/hh.se/ite-en

Description

The recruited person will be expected to teach up to 20% and do research at least 80%, within one or several scientific projects. Research activities will depend on competences and interests, but are expected to build upon our existing portfolio.

The FREEDOM project analyses mobility data, as characterised by two crucially essential dimensions: spatial and temporal. From a technical perspective, obtaining a complete picture requires a framework capable of modelling them simultaneously to take advantage of the insights embedded in the interrelations between the two. Graph Neural Networks (GNNs) is an emerging and promising field of machine learning, in the intersection of deep neural networks and graph theory, that is uniquely suitable to address both aspects.

In the XPM project, we aim to develop several different types of explanations for predictive maintenance (anything from visual analytics through prototypical examples to deductive argumentative systems) and demonstrate their usefulness in four selected case studies: electric vehicles, metro trains, steel plant and wind farms. In each of them, we will demonstrate how the right explanations of decisions made by AI systems lead to better results across several dimensions, including identifying the component or part of the process where the problem has occurred; understanding the severity and future consequences of detected deviations; choosing the optimal repair and maintenance plan from several alternatives created based on different priorities; and understanding the reasons why the problem has occurred in the first place as a way to improve system design for the future. The key technical direction is physics-informed machine learning.

AI and ML is an important part of our education. The recruited person will have an opportunity to advance Bachelor and Master level courses such as Artificial Intelligence, Learning Systems, Data Mining, Applied Data Mining, and Deep Learning. The person will also be involved in the Graduate professional development program (second-cycle courses targeted at the business sector). Finally, the recruited person is expected to participate in supervision of thesis projects for bachelor and master level students, as well as co-supervise PhD students.

Qualifications

The applicant must hold a doctoral degree in Artificial Intelligence/Data Mining/Machine Learning/Information Technology or related fields. The applicant needs to demonstrate a strong research profile in the fields related to topics of interest for CAISR research environment, including recent activities with high impact. The scientific production is expected to be published in high-quality, peer-reviewed research journals and conferences. Documented experience from innovation, research and development in an industrial environment is also a strong merit. The applicant should share the value that diversity and equality among researchers and teachers brings higher quality to research and education. Experience in teaching on advanced level in higher education. Pedagogical training in teaching in higher education.


The person applying for this position also needs to have:

- Ability to conduct research of high international quality in artificial intelligence, specifically data mining and machine learning
- Documented experience from research in collaboration with industrial partners and/or interdisciplinary teams
- Ability to conduct high-quality teaching and to develop courses at different levels
- Experience in supervision of master / PhD students
- Ability to attract external funding
- Dynamism, curiosity, independence, creativity and good teamwor

Salary
Salary is to be determined by negotiation. The application should include a statement of the salary level required by the candidate.

Application
Applications should be sent via Halmstad University's recruitment system Varbi (see link on this page).

The application package shall consist of:
1) a cover letter stating the purpose of the application and a brief statement of why you believe that your goals are well-matched with the goals of this position, together with a description of future research plans

2) a CV that includes at least
- a list of previous degrees, with dates and institutions
- a complete list of publications with 2-3 most relevant ones for this position marked
- a description of previous research and other work experience and links to online copies of the most important publications

3) contact information for at least three references.

For further information, please contact Stefan Byttner(Stefan.Byttner@hh.se) or head of school Magnus Clarin (Magnus.Clarin@hh.se).

 

General Information:

We value the qualities that gender balance and diversity bring to our organisation. We therefore welcome applicants with different backgrounds, gender, functionality and, not least, life experience.