A PhD position is available for a computational biologist at the Laboratory of Computational Biology (www.aertslab.org) together with Center of Human Genetics, KU Leuven. The position will be co-supervised by Prof. Alejandro Sifrim (Laboratory of Multi-omic Integrative Bioinformatics) and Prof. Stein Aerts (Laboratory of Computational Biology).
In this position you will be responsible for the development and implementation of data analysis strategies and pipelines for cutting-edge in situ expression assays, including 10X Genomics Visium and Cartana technologies. This will require novel method development, leveraging state-of-the-art machine (deep) learning technologies to process large-scale imaging and sequencing datasets in a high-performance computing environment.
As part of the newly established Leuven Single-Cell Institute and Spatial Omics Core, the developed analysis pipelines will cater to a broad user base and biological application domains including studies into Crohn’s disease, Parkinson’s disease, and cancer in the host labs.
This project is a close collaboration between the Laboratory of Multi-omic Integrative Bioinformatics (LMIB) and the Laboratory of Computational Biology (LCB), at the Department of Human Genetics (Faculty of Medicine) of the KU Leuven and the Center for Brain and Disease Research at VIB.
The LMIB lead by Prof. Alejandro Sifrim is a newly established team focusing on computational spatial multi-omic method development, from data pre-processing to downstream integrative analyses. We are also part of global initiatives such as the Gut Cell Atlas (part of the Human Cell Atlas) effort. The LCB focuses on computational methods for single-cell regulatory genomics. Some of the recent LCB methods includes SCENIC, cisTopic, and ScoMap.
You will play a key role in the establishment of innovative analytical pipelines for spatial expression profiling technologies and applying these pipelines to various biological question domains.
- Develop and establish computational analysis methods for spatial expression assays leveraging state-of-the-art deep learning technologies.
- Establishing production-proof complete analysis pipelines using containers and NextFlow DSL2, to be useable by other members of the Single-Cell Institute
- Lead the experimental design, data analysis and interpretation of in situ profiling projects, and coordinate with core facilities and experimental biologists.
- To be co-responsible for successful completion of the projects, and play a key role in the publication of the results of these team-collabor ative projects.
- Maintain internal code-bases and documentation.
- Report and communicate on progress in meetings.
- Train multidisciplinary team members in the usage of computational analysis methods.
Most challenging aspects of the role
Establishing analytical workflows for cutting-edge technologies requires a high degree of innovative reasoning, in-depth knowledge of and creativity with computational and statistical methods. Strong data interpretation and problem-solving skills are a must. Given that the field is rapidly advancing, being motivated to learn and keep track of state-of-the-art analysis methods (statistical, machine learning) will be required. High accuracy in work, close coordination and effective communication with other team members and teams will be critical to meet the goals of key biological projects.
Essential knowledge, skills, and experience required
MSc in Bioinformatics, Biostatistics, Artificial Intelligence, Biochemistry, Computer Science or other relevant degree
- Motivation and ambition to make a personal contribution to computational biology research
- Working proficiency in UNIX/Linux
- Proficiency in R and Python script programming languages
- Knowledge of human genetics; genomics, epigenomics, transcriptomics and next-generation sequencing technologies
- Familiarity with the analysis of bulk and single-cell RNAseq data
- Excellent critical and problem-solving skills
- High level communication skills that enable you to evoke complex requirements from, and convey complex information to, individuals with different levels of technical knowledge
- Ability to be inventive and to present novel ideas in method development, data analysis and interpretation
- Ability to work independently and as a team member.
Additional desirable skills and experience
- Experience using classical and/or deep machine learning methods (using TensorFlow/Keras/PyTorch)
- Experience creating and implementing complex data workflows (e.g. Nextflow)
- Experience creating and using containerized computing environments (e.g Docker, Singularity)
- Data visualization expertise (e.g. ggplot, matplotlib, d3)
- Experience with large-scale computational analysis; running software on a high-performance computing cluster or cloud environment
We offer funding for a full-time PhD position (1 year contracts, renewable to 4 years after positive evaluation), but we also invite candidates to apply for (inter)national external funding. You will work in an intellectually challenging and stimulating environment with state-of-the art facilities.
KU Leuven is a research intensive, internationally oriented university that carries out both fundamental and applied scientific research. It is highly inter- and multidisciplinary focused and strives for international excellence. In this regard, it actively works together with research partners in Belgium and abroad. It provides its students with an academic education that is based on high-quality scientific research.
You will work in Leuven, a historic, dynamic and lively city located in the heart of Belgium, within 20 minutes from Brussels, the capital of the European Union, and less than two hours from Paris, London and Amsterdam.
The salary will be in accordance with the University salary scales for doctoral researchers.
For more information please contact Prof. dr. Alejandro Sifrim, mail: email@example.com or Prof. dr. Stein Aerts, mail: firstname.lastname@example.org.
How to apply?
Please complete the online application procedure and include a detailed CV, a motivation letter describing research interests and previous research experience, and a list of publications. For more information: please contact email@example.com