Doctoral student in AI-assisted epidemiological surveillance Full-time NEW

Doctoral Student in AI-Assisted Epidemiological Surveillance

Lund University was founded in 1666 and is repeatedly ranked among the world's top universities. The University has around 46,000 students and 8,500 staff based in Lund, Helsingborg, and Malmö. We are united in our efforts to understand, explain, and improve our world and the human condition.

We are looking for a PhD student who will work on the PhD project Reducing Burden from Infectious Diseases Using AI-Assisted Epidemiological Surveillance and be involved in the group's other research projects.

The successful candidate will join the EPI@Lund research group (Epidemiology and Population Studies) located at the Department of Laboratory Medicine within the Faculty of Medicine. The group is led by Prof. Jonas Björk and is closely associated with the strategic research area EpiHealth (Epidemiology for Health) at Lund and Uppsala universities. The core mission of the research group is to conduct epidemiological research in the intersection between clinical, computational, environmental, and social sciences. A growing research interest is to bring the recent advances within generative AI into epidemiology and public health, where access to an internationally unique data infrastructure with several phenotype-rich cohorts linked with population and health care registers forms the basis for new scientific discoveries.

Our network is interdisciplinary and spans several faculties at Lund University, including disciplines such as computational biology, clinical medicine, infection medicine, social sciences, and law. The group has developed close collaborations with other universities in Sweden, including Umeå, Uppsala, and Stockholm, and we are also part of a national network around epidemiological modeling and collaborate with other researchers both in the Nordics and internationally.

Lund University is a public authority which means that employees get particular benefits, generous annual leave, and an advantageous occupational pension scheme. Our research group has modern office facilities located at Forum Medicum at the central campus areas in Lund which offers opportunities for exchange with other researchers both within and outside the Faculty of Medicine at Lund University. Furthermore, we have a flexitime agreement that provides good conditions for work/life balance.

The overall purpose of the research project is to investigate if innovative AI-assisted systems for epidemiological surveillance, using extensive data from population and health-care registers for the general population together with blood sampling for immunological assessment in targeted subsamples, can reduce burden from infectious diseases among older adults. The utility of such improved surveillance infrastructure will be evaluated for the general population of older adults (65+) living independently or with home care, as well as among those living in nursing homes. The project has four specific research questions (RQs):

• How can population and health care registers in combination be used to monitor protection and frailty among older adults to predict and prevent outbreaks and reduce burden of infectious diseases?
• How can AI and explainable AI (XAI) facilitate improvements in risk predictions and detection of disease outbreaks while at the same time maintain explainability, transparency, and trust of the disease surveillance?
• What is the added value of integrating multimodal data, including biomonitoring data on immunological status in the surveillance of particularly vulnerable groups, for example those living at nursing homes?
• How can our proposed data infrastructure for disease surveillance be used to evaluate public health interventions, including vaccinations and other pharmacological interventions as well as non-pharmacological interventions such as limitation of visitors, use of face masks?

A key research task will be to develop and refine artificial intelligence (AI) methods for analysis of large-scale population and healthcare data. This includes the full AI pipeline in epidemiological research, including data acquisition and linkage across multiple registers and datasets, data preprocessing and quality assessment, handling of missing data, exploratory data analysis, feature engineering, and the development of predictive, temporal, and spatiotemporal models when relevant. The work further includes model testing, validation, benchmarking, and performance evaluation, as well as the application of explainable AI techniques for transparent model interpretation and exploration of frontier large language models (LLMs) and other advanced AI methodologies relevant to epidemiological research. The research task may also include contributions to submissions to the Ethical Review Board and coordination of data requests from register holders.

The doctoral student is expected to actively participate in seminars and other important project meetings and present the project at scientific conferences.

The doctoral student has further responsibility to:

• Seek contact with the colleagues who best support a discussion about the results
• Find avenues for career support
• Formulate, develop, and optimize the ways of communication with supervisors and colleagues that best promote the doctoral student's working methods and aspects

Requirements for employment are: Emphasis will be put on personal qualities. We are looking for someone who is driven, curious, taking responsibility, and have a high motivation. As the research group has a multi-disciplinary and multi-cultural research environment, good communication skills and collaboration skills are required. In addition, the duties require organizational skills and ability to prioritize.

• MSc in applied AI, machine learning, epidemiology, public health, biostatistics, or another related field
• Very good reading comprehension and writing in English
• Strong interest in conducting research in the intersection between immunology, epidemiology, and public health
• Eagerness to acquire a thorough understanding of how immunology, epidemiological reasoning, and quantitative epidemiological methods can be combined with recent advances within AI

Additional qualifications for the position are:

• Experience working with research or research-related work, specifically with epidemiological surveillance or other epidemiological applications of relevance for the project
• Documented ability to manage and analyze large datasets, including population and health care registers, using advanced computational and analytical frameworks, including Python-based machine learning ecosystems, transformer- and foundation-model approaches such as TabPFN, frontier large language models (LLMs), and state-of-the-art explainable AI methodologies (e.g., post hoc explainability and mechanistic interpretability approaches).
• Strong skills in scientific communication, both written and oral

Temporary employment of four years, in accordance with the Higher Education Ordinance- HF 5 kap 7§ in Sweden. The employment is a full term position with the desired start date of July 1st, 2026, or according to agreement.

Students with basic eligibility for third-cycle studies are those who:

• have completed a second-cycle degree
• have completed courses of at least 240 credits, of which at least 60 credits are from second-cycle courses, or
• have acquired largely equivalent knowledge in some other way, in Sweden or abroad.

The employment of doctoral students is regulated in the Swedish Code of Statues 1998: 80. Only those who are or have been admitted to PhD-studies may be appointed to doctoral studentships. When an appointment to a doctoral studentship is made, the ability of the student to benefit from PhD-studies shall primarily be taken into account. In addition to devoting themselves to their studies, those appointed to doctoral studentships may be required to work with educational tasks, research, and administration, in accordance with specific regulations in the ordinance.

Applications are to be submitted via the University's recruitment system. The application must include a personal letter (max 1 page) justifying your interest in the position and how it matches your qualifications. The application should also include a CV (max 2 pages) and degree certificates with copies of grade transcripts. Applicants must also provide contact details of 3 referees who have agreed to provide a letter of recommendation. Please, note that incomplete applications will not be evaluated.