Postdoctoral Researcher in Machine Learning & Predictive Modelling

We are looking for a highly motivated and dynamic postdoctoral researcher for a 3-year position, who is a specialist in predictive modelling to commence 1st December 2024. We are seeking a highly motivated candidate with experience in statistical machine learning to work on methods that combine health records and epidemiological data to develop robust applicable risk prediction models. We are looking for a candidate who has experience in, but not restricted to: implementing machine learning approaches (penalised regression, tree-based algorithms, Super Learner, deep learning models) in real-world scenarios, and analysing large data of electronic health records and nationwide registries.

Information on the group can be found at: mlgh.net



Our research
The Section of Epidemiology actively contributes to advancing theoretical epidemiology, with a particular emphasis on causal inference, complexity, and life course epidemiology. Our research explores the dynamic interactions among genes, the environment, and health throughout the life course and across generations. As a Post Doc, you would become a member of The Computational and Mathematical Global Health Group, co led by Professor Samir Bhatt and Associate Professor David Duchene located at the Section of Epidemiology at University of Copenhagen. We are a world leading group that focuses on a diverse range of topics. We research at the interface between computer science, mathematics, biology, and epidemiology. The post doc position is in close collaboration with the machine learning and global health network mlgh.net. The position will be highly collaborative and crosscut/support multiple large projects in the group.



Your job

We are looking for a specialist in risk prediction modelling. The ideal candidate for this job would have had extensive experience developing and validating classic statistical and machine learning models in real world scenarios, preferably in a clinical framework. Knowledge of epidemiology, study designs and causal inference would be considered a significant advantage. Your day-to-day tasks would be leading applications of statistical and machine learning models to a range of problems in the group including public health, biology, and economics. You will be in charge of developing models in tandem with other researchers in the group, with a focus in applicability. The ideal candidate would already have extensive experience implementing various learners using R and/or Python.





Profile
We are looking for a highly motivated and enthusiastic scientist with the following competencies and experience:

Essential experience and skills:

• You have a PhD in epidemiology, data science, bioinformatics, statistics
• You are highly experienced in applying, developing and implementing a range of machine learning models
• You have an active interest in machine learning and statistics
• Proficient communication skills and ability to work in teams
• Excellent English skills written and spoken

Desirable experience and skills:

• Experience/knowledge of R and Python
• Experience/knowledge in machine learning frameworks, e.g. tidymodels, mlr3, scikit-learn
• Experience/knowledge in validation of (clinical) risk prediction models
• Experience in epidemiological analyses of registry data and causal inference methods
• Publications or preprints related to predictive modelling

Place of employment

The place of employment is at the Section of Epidemiology, University of Copenhagen. We offer creative and stimulating working conditions in a dynamic and international research environment.



Terms of employment
The average weekly working hours are 37 hours per week.

The position is a fixed-term position limited to a period of 3 years. The starting date is 1st December 2024 or thereafter.

Salary, pension and other conditions of employment are set in accordance with the Agreement between the Ministry of Taxation and AC (Danish Confederation of Professional Associations) or other relevant organisation. Currently, the monthly salary starts at 38,500 DKK/approx. 5,100 EUR (April 2024 level). Depending on qualifications, a supplement may be negotiated. The employer will pay an additional 17.1 % to your pension fund.



Foreign and Danish applicants may be eligible for tax reductions if they hold a PhD degree and have not lived in Denmark the last 10 years.



The position is covered by the Job Structure for Academic Staff at Universities 2020.



Questions
For further information please contact Professor Samir Bhatt, Department of Public Health, [email protected] or [email protected]



Application procedure
Your application must be submitted in English by clicking ‘Apply now’ below. Furthermore, your application must include the following documents/attachments – all in PDF format:

1. Curriculum vitae
2. Diplomas (Master and PhD degree or equivalent). If the PhD is not completed, a written statement from the supervisor will do.
3. List of publications

Deadline for applications: 8/9/2423.59pm CET

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.



The further process
After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the hiring committee. All applicants are then immediately notified whether their application has been passed for assessment by an unbiased assessor. Once the assessment work has been completed each applicant has the opportunity to comment on the part of the assessment that relates to the applicant him/herself.

You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/



The applicant will be assessed according to the Ministerial Order no. 242 of 13 March 2012 on the Appointment of Academic Staff at Universities.



Interviews are expected to be held in week 40

The University of Copenhagen wish to reflect the diversity of society and encourage all qualified candidates to apply regardless of personal background.