PhD studies for Graduates in Bioinformatics for the project “Computational methods for detection of cancers and their mutational imprints from sequencing of blood cell-free DNA” is available in the Department of Clinical Medicine, Aarhus University, Denmark (Computational Biology, Machine Learning, Bioinformatics PhD Programs with Scholarships)
Deadline to Apply
May 01, 2021 (23: 59 GMT +1)
|No. of Position(s)||Few positions|
|Research Area||– Bioinformatics|
– Computational Biology
– Machine Learning
|Scholarship||According to Standard Norms|
|Workplace||Department of Molecular Medicine (MOMA)|
Department of Clinical Medicine
Faculty of Health
|Contract Period||Preferred starting date 1 July 2021|
- Master’s degree within bioinformatics, statistics, computer science or a related discipline.
- Proficiency in at least one programming language is required.
- Experience with handling of genomics data is advantageous.
- Knowledge about or strong interest in statistical modelling is advantageous.
- Knowledge about cancer biology and cell-free DNA is advantageous.
- Proficiency in both oral and written English is required.
- You will create new computational methods that combine classical statistical models with modern machine learning (e.g. neural nets) for finding those cancer “needles” in the blood “haystack”.
- You will both work on developing statistical models for determining ctDNA load and variants from whole-genome sequencing of cfDNA as well as methods for deep, targeted sequencing data.
How to Apply?
To apply, please click APPLY ONLINE button mentioned above.
For information about application requirements and mandatory attachments, please see our application guide.
About the Project
Blood cell-free DNA (cfDNA) is emerging as an important biomarker in cancer. Several studies have already demonstrated how it can both be used to determine whether cancer is present in blood or not, and act as a “liquid tumour biopsy”, which makes it possible to investigate the mutational profile of a tumour.
Sequencing is becoming the dominant approach for analysing cfDNA, yet finding signals of cancer let alone specific mutational events is difficult. First, because every tumour fragment is typically outnumbered by thousands of “background” fragments. Second, because technical artifacts originating e.g. from partial degradation of DNA may look a lot like real mutations.
All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.
Lasse Maretty Sørensen