PhD student in Epidemiological surveillance - Hiring in process/Finished, not possible to apply

Description of the workplace
Our interdisciplinary research team at the Department of Laboratory Medicine within the Faculty of Medicine conducts research in infectious disease epidemiology and pandemic preparedness. The team is highly interdisciplinary – ranging from Infection Medicine, Epidemiology, Immunology to Computational Biology and Medical Law - and has extensive national and international collaborations. You can read more about the project on our websites research portal and lupop.

The research project is led by Professor Jonas Björk (Epidemiology) and Associate Professor Malin Inghammar (Infection Medicine) and includes a further > 10 staff members at different departments and faculties. The group is international and comprises both research and technical expertise. We are now looking for a PhD student in Epidemiological surveillance using big data and machine learning methods. As a workplace, we safeguard a positive work environment with respect and consideration in our relations with one another.

What we offer
Lund University is a public authority, which means that you will have special benefits, generous annual leave and an advantageous occupational pension. We also have a flexitime agreement that creates good conditions for work/life balance. Read more on the University’s website about being a Lund University employee, Work at Lund University

Work duties and areas of responsibility
The overall aim of this PhD project within Medical science – research area: Epidemiology -  is to investigate how preparedness for future pandemics and other health crises in society can be improved by using big population data in combination with adequate analytical approaches for epidemiological surveillance.

The project will use extensive population and health care registers, both from regional and national sources. We will also use population data from other sources such as mobility patterns and internet searches. It will also be relevant to include simulated data on e.g. adverse events or new biological agents appearing in society.

We will study the general population but also specific groups such as older people living in special houses (SÄBOs) to predict the risk of local outbreaks in groups that are particularly vulnerable to infectious diseases.

The successful candidate will develop new AI and machine learning methods to detect unusual patterns or adverse events in large volumes of healthcare and population data. The models will be based on deep learning techniques, such as autoencoders and variational autoencoders and will be compared with traditional methods for similar surveillance tasks, e.g. local outlier factor and k-means clustering. An important aspect of the model development is to efficiently make use of the available data sources, where sparsity pose a particular challenge. Evaluation metrics of the proposed methods will also be an important research topic. The usefulness of applied AI methods and machine learning methods will be compared with more traditional statistical methods e.g. based on regression modelling.

Qualifications
The requirements for the position are: (grey text are examples)

• A Master’s degree in Epidemiology, Public Health, Computational engineering or related field
• Documented experience in applied artificial intelligence and machine learning methods
• Very good knowledge of English
• Good analytical ability, drive and a good ability to collaborate
  
  

Great emphasis will be placed on personal characteristics. The assignments require that you have the ability to work independently as well as the ability to work well with others in the research group and researchers both within and outside the subject area.

Additional qualifications for the position are: (grey text are examples)

• Previous experience of research or other high-level work in similar projects
• Experience in using population and health care registers for epidemiological surveillance or monitoring of side effects from vaccinations
• Knowledge of Swedish or Scandinavian register infrastructures
• Very good knowledge of Swedish

Further information
Eligibility
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.

Type of employment
Limit of tenure, four years according to HF 5 kap 7§.

How to apply
You apply for the position via the University’s recruitment system. The application is to include a personal letter describing how you meet the qualification requirements and why you are interested in the position. The application is also to include a CV with a list of publications, a general description of previous research work, a copy of your doctoral degree and any other documents to which you wish to draw attention (copies of degree certificates, grade transcripts, details of referees, letters of recommendation, etc.)