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

Halmstad University
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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.

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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).

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Description

The PhD student is expected to do research, in one or more cutting-edge AI/ML topics, including data mining, 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, and more.

In this era of technological advancement, the focus has shifted towards more efficient and environmentally responsible monitoring of industrial systems. The integration of AI/ML analytics is paramount for prompt identification of inefficient and suboptimal operations, especially given the always-increasing complexity of modern systems and the demands on environmentally sustainable operations. This creates significant challenges for the industry since it often means technology shifts (e.g., electric vehicles or new fuel mixes) where learning must be done from prohibitively few examples. Previously reliant on labour-intensive human input (supervised ML), the paradigm is shifting towards automated, self-regulating systems requiring minimal human oversight. These systems are characterised by the need to understand and capture context while learning from available operational data, typically with only a handful of human expert labels attached, to identify and address issues autonomously. The necessary next step is AI/ML methods that can process the available data, which are largely unlabelled and only superficially understood.

This is a full-time position available from January 1st, 2025 (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's degree in machine learning or related discipline. 
• 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. 
• Excellent oral and written communication in English. 

Students expecting to finalize their degree the coming month are also welcome to apply. Only those who are or have been admitted to third-cycle courses and study programs 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.

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Information for International Applicants
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