Publicerad: 2025-05-19
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.
https://hh.se/english/about-the-university/vacant-positions.html
The School of Information Technology
Halmstad University’s School of Information Technology (ITE) is a renowned multi-disciplinary institution with around 155 employees from 20 different countries. ITE is internationally recognised for its applied research and collaborative initiatives, focusing on smart technology and its practical applications.
Within ITE, our students and researchers engage in diverse areas of study, including electronics, AI, information-driven healthcare, autonomous vehicles, social robotics, and digital design. We offer a comprehensive range of educational programmes, ranging from undergraduate to doctoral levels, as well as professional development opportunities.
Research is conducted within the University’s research programmes, especially Information Driven Care (IDC), Re-Imagining Future Smart Living – beyond the Living Lab (REBEL), Learning in a Digitalised Society (LeaDS) and the Future Industry Research Programme (FIRP).
ITE is home to Leap for Life, an innovation centre for information-driven care, as well as the Electronics Centre in Halmstad (ECH), a collaborative space for electronic development.
http://www.hh.se/ite-en
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 research will be connected to the KEEPER project.
This is a full-time position available from September1st, 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).
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 http://hh.se/english/discover/discoverhalmstaduniversity.9285.htm
Information for International Applicants
Choosing a career in a foreign country is a big step. Thus, to give you a general idea what we have to offer in terms of benefits and life in general for you and your family/spouse/partner please visit:
https://www.hh.se/english/about-the-university/vacant-positions/international-staff-support.html
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.
https://hh.se/english/about-the-university/vacant-positions.html
The School of Information Technology
Halmstad University’s School of Information Technology (ITE) is a renowned multi-disciplinary institution with around 155 employees from 20 different countries. ITE is internationally recognised for its applied research and collaborative initiatives, focusing on smart technology and its practical applications.
Within ITE, our students and researchers engage in diverse areas of study, including electronics, AI, information-driven healthcare, autonomous vehicles, social robotics, and digital design. We offer a comprehensive range of educational programmes, ranging from undergraduate to doctoral levels, as well as professional development opportunities.
Research is conducted within the University’s research programmes, especially Information Driven Care (IDC), Re-Imagining Future Smart Living – beyond the Living Lab (REBEL), Learning in a Digitalised Society (LeaDS) and the Future Industry Research Programme (FIRP).
ITE is home to Leap for Life, an innovation centre for information-driven care, as well as the Electronics Centre in Halmstad (ECH), a collaborative space for electronic development.
http://www.hh.se/ite-en
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 research will be connected to the KEEPER project.
This is a full-time position available from September1st, 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).
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 http://hh.se/english/discover/discoverhalmstaduniversity.9285.htm
Information for International Applicants
Choosing a career in a foreign country is a big step. Thus, to give you a general idea what we have to offer in terms of benefits and life in general for you and your family/spouse/partner please visit:
https://www.hh.se/english/about-the-university/vacant-positions/international-staff-support.html