PhD Position in Applied Health Technology - AI for Data-Driven Precision Health

Om jobbet

Blekinge Institute of Technology is a place of curiosity and ingenuity with a campus in the middle of the Blekinge archipelago. Here we offer education from undergraduate to doctoral level and conduct world-class applied research. We change and make a difference. Do you want to become one of us?

The Department of Health is one of six departments within the Faculty of Engineering. Our education and research combine technological innovation with applied science in order to promote human well-being and quality of life in a digitalised world.

Applied Health Technology at BTH is a transdisciplinary research field that explores how technology can support health and sustainability through digitalisation, data-driven systems, and intelligent decision support.

About the position

This PhD position is linked to the ELLIIT project New Machine-Learning Methods for High-Dimensional, Population-Scale Health Data, conducted in collaboration with Lund University. The project aims to develop and apply advanced AI methods to extract meaningful knowledge from large-scale, heterogeneous, and high-dimensional biomedical and population data, often referred to as Big Data in the health and life sciences. The overall objective is to contribute to next-generation decision support and precision health.

At BTH, the PhD project will focus on computational and methodological development, with particular emphasis on efficient and scalable training, model interpretability and explainability, and reproducibility in high-dimensional machine learning frameworks. The project aims to advance the research frontier in explainable AI for large-scale and complex datasets by developing algorithms, pipelines, and tools suitable for critical decision-making contexts.

The doctoral student will be based at the Health Technology Research Lab and will work in close collaboration with Lund University and the SNAC-Blekinge study, which provides access to large population-based datasets. The project is an integrated part of a broader ELLIIT collaboration, in which BTH leads computational and applied AI development.

Research focus

The PhD project will focus on the development of scalable and efficient machine learning approaches for analysing large, multimodal health data, with particular attention to interpretability, explainability, robustness, and reproducibility. Relevant methods may include, for example, model interpretation techniques, explainable AI approaches, and transparent computational workflows.

The work may also involve implementation and optimisation of algorithms in computationally demanding environments, as well as the development of interpretable AI frameworks suitable for real-world decision support systems

The exact research direction will be developed in dialogue with the admitted doctoral student, taking into account the candidate's background and areas of interest.

Eligibility requirements

To be employed as a doctoral student, the applicant must be admitted to doctoral education.

Meritorious qualifications

Selection is based on the assessed ability to successfully complete doctoral studies. Particular weight will be given to applicants with a Master's degree in computer science, data science, applied mathematics, machine learning, signal processing, or another closely related technical field.

The applicant should also demonstrate an interest in health technology and the ability to work with complex and large-scale health data using advanced machine learning and statistical methods, strong analytical skills and solid programming experience, very good English communication skills, both written and spoken, the ability to work independently and engage in scientific reasoning, and good collaborative and communication skills.

Documented experience of, or a strong interest in, one or more of the following areas is considered an advantage: machine learning, data analysis, statistical modelling, explainable AI, computational methods for large-scale data, and analysis of biomedical or population-based datasets.

An interest in applications in medicine or health sciences is desirable, but previous experience is not required.

Place of employment: Karlskrona, Sweden

Employment level: Full-time (100%)

Commencement: By agreement, from summer/autumn 2026

Duration: The position is a fixed-term full-time doctoral appointment for up to four years. The appointment is initially for one year and may thereafter be renewed for up to two years at a time, provided that the doctoral studies progress according to plan. The position may also include departmental duties corresponding to up to 20% of full-time employment, in which case the total period of employment may be extended to a maximum of five years.

Application deadline: 2026-05-25

The application must include a personal letter describing your motivation, your research interests, and possible directions within the field, a CV, certified copies of degree certificates and academic transcripts, contact details for two referees, and a list of publications and/or a link to your Master's thesis.

You apply online through our recruitment system by clicking on the "apply" button below.

If you have protected personal data, you should not register your application in our recruitment system. Instead, contact the HR Partner listed as the contact person in the advertisement for further instructions.

Instructions to applicants can be found at https://www.bth.se/english/about-bth/work-at-bth/vacancies. It is the responsibility of the applicant to ensure that the application is complete in accordance with the advertisement and instructions.

In case of different interpretations of the English and Swedish versions of this announcement, the Swedish version takes precedence.

Blekinge Institute of Technology, BTH, embraces diversity and welcomes applicants with varying backgrounds and experiences.
We work actively to take advantage of the qualities that diversity and an even gender distribution contribute to our operations.

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Contact person

Peter Anderberg

Professor

+46455385436

peter.anderberg@bth.se

Terese Lindberg

Prefekt/Head of Department

+46455385477

terese.lindberg@bth.se

Helena Rosenqvist

HR-partner

+46455385208

helena.rosenqvist@bth.se

Mikael Åsman

Facklig representant (SACO)

+46455385720

mikael.asman@bth.se

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