Professor in machine learning - Hiring in process/Finished, not possible to apply

Halmstad University
Work at a University where different perspectives meet!

Halmstad University adds value, drives innovation and prepares people and society for the future. Since the beginning in 1983, innovation and collaboration with society have characterised the University's education and research. The research is internationally reputable and is largely conducted in a multidisciplinary manner within the University's two focus areas: Health Innovation and Smart Cities and Communities. The University has a wide range of education with many popular study programmes. The campus is modern and well-equipped, and is situated close to both public transportation and the city center.

More information about working at Halmstad University: https://hh.se/english/about-the-university/vacant-positions.html

The School of Information Technology
Halmstad University consists of four interdisciplinary Schools and the current position is located at the School of Information Technology (ITE). ITE is a multicultural school with around 130 employees from 20 different countries. It is a strong research and education environment, with focus on smart technology and its applications. Students and researchers are working with everything from AI and information driven care to autonomous vehicles, social robotics and digital design. ITE offers education on all levels, from undergraduate to PhD education, plus education for professional. Research is conducted within aware intelligent systems, smart electronic systems, cyber physical systems and digital service innovation. These four areas constitute the four technology areas of ITE . An innovation centre for information driven care called Leap for Life is connected to ITE, as well as a collaboration arena for electronic development, Electronics Centre in Halmstad (ECH)

More information about the School of Information Technology: hh.se/ite-en

Description
The position is based at the School of Information Technology at Halmstad University, and will be part of the strategic research area within IT and mobile communications, ELLIIT (http://www.elliit.se), a collaboration between Linköping University, Lund University, Halmstad University, and Blekinge Institute of Technology. The successful candidate is expected to contribute to ELLIIT’s broader goals within research, graduate education, dissemination, and exploitation.

Halmstad University aims to expand its strengths in machine learning (ML) and artificial intelligence (AI) as increasingly important drivers of society’s digitalization. Our partners can use AI/ML to tackle better challenges related to electromobility, and autonomous driving, as well as efficiency, reliability, and resilience of Industry 5.0. The public sector, including healthcare, can use AI/ML for patient-centred care to increase quality while reducing the costs of services.

The professor is expected to do research, both independently and in collaboration with other members of the CAISR research group, across various cutting-edge AI/ML topics, including data mining, affective computing, context-aware systems, knowledge-based intelligent systems, representation learning, meta-learning, transfer learning, multi-task, self-supervised and weakly-supervised learning, federated learning, anomaly detection, synthetic data generation, graph neural networks, evaluation, verification and validation of intelligent systems, and more.

An example challenge addressed across several CAISR projects is how to jointly learn data representations that are useful for multiple tasks, both from data and expert knowledge, allowing for autonomous adaptation to a specific task. It includes physics-informed (and knowledge-engineered in other ways) machine learning, aware systems research, and autonomous knowledge creation. These directions are well-aligned with the ELLIIT Excellence Center Technology Foresight, particularly with the “Big data and network science” and “Industry 4.0/5.0” focus themes; they directly address the “AI, large-scale algorithms, machine learning, deep learning, and XAI” research challenge and are relevant for several others.

From the application perspective, much of CAISR research concerns mobility data, characterized by two crucially essential dimensions: spatial and temporal. Obtaining a complete picture requires a framework capable of modeling 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. One challenge is extracting general features suitable for more than one task, often in self-supervised or semi-supervised settings.

At the same time, similar techniques can be applied to, for example, healthcare data. A rich and efficient representation of patient trajectories is essential for precision healthcare, capturing the temporal disease progression and critical correlations. Most solutions neglect the data fusion part, where many relevant sources of information enrich the key concepts, especially with human-in-the-loop approaches.

The importance of explainability for AI/ML models is also growing. Different types of explanations (from visual analytics through prototypical examples to deductive argumentative systems) are needed for different applications, such as healthcare or predictive maintenance. One challenge is demonstrating how proper explanations of AI decisions lead to better results across several dimensions.

Other directions of CAISR research include physics-informed machine learning, aware systems research, and autonomous knowledge creation. AI and ML are also essential parts of our education. The recruited professor can be involved in Bachelor’s and Master’s level courses such as Artificial Intelligence, Learning Systems, Data Mining, Applied Data Mining, and Deep Learning, including a newly started “Applied Artificial Intelligence” BSc program, graduate professional development program (second-cycle courses targeted at the business sector), supervision of thesis projects, outreach activities, and more.

Qualifications
Professor level competent:
"A person who has demonstrated both research and teaching expertise shall be qualified for employment as a professor. The assessment criteria for appointment as a professor shall be the degree of the expertise required as a qualification for employment. As much attention shall be given to the assessment of teaching expertise as to the assessment of research or artistic expertise. Each higher education institution determines itself what assessment criteria are otherwise to apply to the appointment of a professor (Higher Education Ordinance Chapter 4 Section 3). " Read more here.

Broad international network

For appointment as a professor, the following assessment criteria will be applied:

• Ability to conduct research of high international quality in artificial intelligence, specifically machine learning
• Ability to conduct research independently
• 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 teamwork
• Willingness to address opportunities and challenges within AI and machine learning

Salary
Salary is to be settled 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).

How to design your application

For further information, please contact Stefan Byttner(Stefan.Byttner@hh.se).

General Information
We value the qualities that gender balance and diversity bring to our organization. We therefore welcome applicants with different backgrounds, gender, functionality and, not least, life experience.
Read more about Halmstad University at http://hh.se/english/discover/discoverhalmstaduniversity.9285.html