Doctoral student (Licentiate) in machine learning

Halmstad University; Halmstads kommun; On-site; SEK 420,000 - 480,000; Full time

Job summary

Halmstad University’s School of Information Technology invites applications for a doctoral student position in information-driven care research. The role focuses on AI/ML methods and healthcare data within the triple-helix collaboration with regions, municipalities and industry partners.

The position includes teaching and supervision up to 20%, coursework, and the potential to extend to a full PhD after licentiate depending on funding. Fluency in English and Swedish is preferred.

Qualifications

• M.Sc. degree in computer science, computer engineering, electrical engineering, Physics, or related field with focus on machine learning and AI.
• Excellent communication skills in English and Swedish.
• Strong knowledge of machine learning with experience in one or more areas such as representation learning, vertical federated learning, time-series analysis, uncertainty quantification, or deep learning.
• Experience in related areas such as multimodal data fusion, sequential/trajectory modelling, graph-based learning, explainable AI, causal inference, or privacy-preserving ML.
• Proficiency in Python.
• Experience in healthcare applications and interdisciplinary research; prior research experience (thesis/publications).
• Publications in high-tier AI/ML venues.

Responsibilities

• Conduct doctoral-level research towards a licentiate degree in information-driven care research program.
• Analyse patient trajectories across the full care chain with multi-source data.
• Publish research findings in peer-reviewed venues.
• Complete required doctoral coursework as part of the licentiate programme.
• Participate in activities with regions, municipalities, and industry partners within the triple-helix model.
• Teaching and supervision duties up to 20% may be included.

Skills

English & Swedish communication

Machine learning knowledge

Python programming

Healthcare research experience

Publications in AI/ML

Interdisciplinary research

Education

M.Sc. in computer science / computer engineering / electrical engineering / Physics with ML/AI focus

Tools

Python

Job description

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 centre.

More information about working at Halmstad University

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.

More information about the School of Information Technology

Description

The information-driven care research program has a continued need to strengthen doctoral-level research on AI and ML in healthcare, encompassing main research projects such as CAISR Health, a KK Foundation-funded research profile on responsible and trustworthy AI/ML in healthcare, and Informationsdriven nära vård, which analyses the full patient care chain and trajectory to identify how AI, ML and information-driven approaches can provide more insights to improve care. The program needs a doctoral student to conduct research towards a licentiate degree within this area, contributing new methods and insights within the triple-helix collaboration between academia, regions, municipalities, and industry partners.

Tasks include:

• - Conduct doctoral-level research towards a licentiate degree within the information-driven care research program, focusing on one or more of the following research areas: Representation learning, Vertical federated learning, Time-series analysis and modelling, Uncertainty quantification, Multimodal / heterogeneous data fusion, Sequential and trajectory modelling, Graph-based learning, Explainable and interpretable AI, Causal inference, Privacy-preserving machine learning
• - Analyse patient trajectories across the full care chain, working with multi-source clinical and care data from regions, municipalities, and industry partners
• - Deliver activities in relation to the broader research project portfolio (e.g. CAISR Health and Informationsdriven nära vård), including analysis, data preparation, and reporting
• - Publish research findings in peer-reviewed scientific venues
• - Complete relevant doctoral coursework as part of the licentiate programme
• - Participate actively in the information-driven care research program’s activities and in collaboration with regions, municipalities, and industry partners within the triple-helix model
• - Teaching and supervision duties (up to 20%) can be included

This is a position to a licentiate degree. Subject to securing additional funding, there may be a possibility to extend the position and continue towards a full PhD after the licentiate degree.

Qualifications

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).

• - A M.Sc. degree (or equivalent, corresponding to at least 240 ECTS credits including a degree project) in computer science, computer engineering, electrical engineering, Physics, or a closely related field with focus on machine learning and AI.
• - Good communication skills in English and Swedish are required.
• - Strong knowledge of machine learning is required, with experience in one or more of representation learning, (vertical) federated learning, time-series modelling, uncertainty quantification, or deep learning, including transformer-based and more recent architectures like Mamba.
• - Experience in one or more related areas such as multimodal data fusion, sequential and trajectory modelling, graph-based learning, explainable AI, causal inference, or privacy-preserving machine learning is required.
• - Programming skills in Python are required.
• - Experience in healthcare applications and interdisciplinary research is required, as is prior research experience (e.g. thesis work or publications).
• - Relevant publications in high-tier venues within AI and ML are required.

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.

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.

Information for International Applicants

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