PhD position work on Bioinformatics PhD project: developing tumour-agnostic methods to classify cancer from circulating tumour DNA sequence data, at the Department of Molecular Medicine (MOMA), Department of Clinical Medicine at the Faculty of Health, Aarhus University, Denmark
Deadline to Apply
June 01, 2021 (23: 59 GMT +1)
|No. of Position(s)||1|
|Research Area||– Bioinformatics|
– Computer science
|Scholarship||According to standard norms|
|Workplace||Department of Molecular Medicine (MOMA)|
Department of Clinical Medicine
|Contract Period||Not specified|
|Starting date||Sep 01, 2021|
In order to be assessed as qualified for a PhD study, you hold – or soon will – a Master’s degree within bioinformatics, statistics, computer science or a related discipline.
- You should furthermore have the following skills:
- Proficiency in statistical data analysis and scientific computing, preferably with fluency in R or Python
- Fluency in written and spoken English
- Collaborative attitude
- Prior experience with HPC environments and with handling and analysing NGS-based data is preferable, but not required.
- As a person, you are ambitious, take ownership and possess good communication and interpersonal skills. We expect you to be fluent in oral and written English.
Circulating tumour DNA (ctDNA) obtained from liquid biopsies is emerging as an important biomarker in cancer. Several studies have demonstrated how ctDNA can be used to establish the presence of cancer following tumour resection, and also act as a “liquid tumour biopsy” to determine the tumour mutational profile.
Sequencing is becoming the dominant approach for analysing ctDNA, yet finding signals of cancer let alone specific mutational events is difficult. This is because every tumour fragment is typically outnumbered by thousands of “background” fragments, and because technical artefacts such as those caused by sequencing errors or partial degradation of DNA may appear like real mutations. While currently most strategies to monitor cancer using ctDNA starts with sampling and sequencing the primary tumour, in this PhD-project you will create new methods that can detect the presence of a tumour without first analysing a tumour tissue sample. Instead of looking for a single mutation known to exist in the tumour, this project focus on exploring several cancer signals from plasma sequencing data and combining them into a series of classification calls.
The project will focus on analysing ctDNA data produced from bladder and colorectal cancer patients. At MOMA we have generated large datasets consisting of several thousand plasma samples collected at individual time points from cancer patients undergoing treatment. These plasma samples have been analysed using whole genome and panel sequencing, with paired data from their primary tumour.
Together with other researchers at MOMA including experienced translational genomics scientist and bioinformaticians from multiple research groups you will work as part of a team effort to develop novel tools for efficiently extracting a diverse array of ctDNA features from sequence data. There will be a particular focus on developing Machine Learning methods to combine ctDNA features into cancer-specific estimates, such as total tumour load, cancer growth rate, tumour clonality, tumour immunogenicity, and specific and overall somatic mutation and copy number burden.
You will be working at the Department of Molecular Medicine (MOMA), Department of Clinical Medicine at the Faculty of Health, Aarhus University. MOMA offers a vibrant and unique interdisciplinary research environment with several experimental and informatics groups focusing on translational cancer research powered by genomics.
Additionally, we expect the successful candidate to also take part in many other ongoing projects on methods development and/or translational approaches within the field of cancer evolution, immunology and precision medicine. Data sources particularly include sequence data (including WGS, WES and panel-based) from multiple patient-relevant sources (tumour/normal/plasma).
As a PhD student your position is primarily research-based but may also involve teaching assignments. You will contribute to the development of the department through research of high international quality. In your daily work, you will work closely with colleagues on your project, where you will receive supervision and guidance.
Your main tasks will consist of:
- Independent research of high international quality, including publication.
- Analysis of ctDNA NGS data
- Development of methods and pipelines to infer ctDNA features
- Integration of large datasets and orthogonal data types
- Statistical analysis
- Presentation of project data and results at international conferences
How to apply?
To apply, please click the Apply Online button mentioned above
For information about application requirements and mandatory attachments, please see our application guide.
About the project/department
MOMA offers a vibrant and unique interdisciplinary research environment with more than 20 years’ experience in genomics, transcriptomics, and translational cancer research. The department has close clinical ties and houses state-of-the-art laboratory facilities, comprehensive cancer biobanks and significant next-generation sequencing facilities with access to large HPC facility and extensive genomics data infrastructure developed in collaboration between Aarhus University and Aarhus University Hospital (https://moma.dk/). MOMA also participates in the Danish National Genome Center, hosting part of the sequencing facilities.
MOMA hosts both wet lab-centered research groups focusing on bladder, colon and prostate cancer, and bioinformatics groups with strong ties to and dual affiliations with the Bioinformatics Research Centre (BiRC) at Aarhus University.
- It is expected to conduct interviews during June-July 2021.
- All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.
Tel.: (+45) 7845 5347
Tel.: (+45) 7845 5366
Post expires at 8:59am on Wednesday June 2nd, 2021 (GMT+9)