Doctoral student in machine learning

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: http://www.hh.se/ite-en

Description
This PhD position is part of the CAISR project. 

The selected PhD student will carry out research in collaboration with other members of the CAISR research group, across a variety of cutting-edge AI/ML topics, including representation learning, meta-learning transfer learning, multi-task, self-supervised and weakly-supervised learning, anomaly detection, deep learning, and more.

An example is jointly learning data representations that are useful for multiple tasks, allowing for autonomous adaptation to a specific task. This is an important topic as it allows for autonomous adaptation to a specific task, by learning features that are most appropriate.

The importance of explainability for AI/ML models is also growing. We are developing several different types of explanations (anything from visual analytics through prototypical examples to deductive argumentative systems) for different applications, such as healthcare or predictive maintenance. One challenge is to demonstrate how the right explanations of decisions made by AI systems lead to better results across several dimensions, including identifying where the problem has occurred; understanding the severity and future consequences of detected deviations; choosing the optimal treatment, repair or maintenance based on different priorities; and understanding the reasons why the problem has occurred in the first place as a way to improve for the future.

Other directions of CAISR research include physics-informed machine learning, aware systems research and autonomous knowledge creation

The employment also includes teaching responsibilities corresponding to a maximum of 20% of full-time.

This is a full-time position available from February 1st 2023 (or as soon as possible) for a period of four years to a PhD degree (extended with one year after one year, subject to satisfactory progress of the PhD study). 

Qualifications
The ideal candidate has a Master’s degree in computer science, machine learning, robotics, mathematics, mechanical engineering or a related engineering discipline.

The candidate must have a strong background in machine learning, artificial intelligence, data mining, or signal processing is desirable.

Excellent programming skills, analytical problem solving and organizational abilities are required.

Only those who are or have been admitted to third-cycle courses and study programmes at a higher education may be appointed to doctoral studentships. (The Higher Education Ordinance Chapter 5 Section 3). The student’s ability to benefit from doctoral studies will be taken into account when we make the appointment. (The Higher Education Ordinance Chapter 5 Section 5).

Salary
Doctoral students are employees of the University and paid a salary according to a uniform salary scale, adjusted in relation to the progress in education.

 

Application
Applications should be sent via Halmstad University's recruitment system Varbi (see link on this page). 
https://www.hh.se/english/about-the-university/vacant-positions/how-to-apply.html



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