Doctoral student 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
This PhD position is part of the FFI project "AIM-TRUE". 

The selected PhD student will carry out research in collaboration with other members of the "AIM TRUE" research group.

The research topics are connected to the AIM-TRUE project where the goal is to develop new ML-based, flexible, and green services for automotive logistics that reduce costs while increasing customer satisfaction and maintaining a competitive advantage. All these goals can only be achieved by anticipating where and when a part will be needed and delivering that part to the correct region before this need even arises, thus reducing costs and increasing service levels. The AIM-TRUE (AI-driven Automotive Service Market: Towards more Resource-Efficient and Sustainable Vehicle Maintenance) project focuses on using state-of-the-art methods based on meta-learning to improve the services provided by the Service Market. In particular, more predictability enables the use of environmentally friendly transport channels and reduces the scrapping of parts due to obsolescence. AIM-TRUE will leverage ML to better understand the factors affecting parts availability and enable individualised inventory control policies. The project’s primary goal is to improve heavy-duty aftermarket resource efficiency and sustainability by reducing three aspects: urgent transport orders, back-and-forth haulage, and part scrapping. The new generation of predictive logistics provides opportunities for better system understanding, large-scale optimisation, quality monitoring, and new data-driven innovative services, all of which are prerequisites for the efficient use of resources – while providing the right parts at the right place and time.     

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

This is a full-time position available from September 1st 2024 (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). Since the employment also includes teaching responsibilities corresponding to a maximum of 20% of full-time, the position is extended with the same amount of time as the teaching activities.

Qualifications
The ideal candidate has a Master degree in machine learning or 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). 
How to design your application 

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