Programme Thesis
This EMBO Practical Course funds intensive training in causal inference and causal representation learning for biomedical researchers, aiming to equip participants with the skills to move beyond associational analysis to cause-and-effect reasoning in clinical, genomic, and molecular data. It exists to address the growing need for rigorous causal methods in biomedicine, enabling better treatment evaluation, mechanistic understanding, and drug target identification.
Selection Criteria
- Eligibility: PhD student, post-doctoral researcher, or research scientist working with clinical healthcare and/or molecular data.
- Relevance: Applicant's current work must involve computational biology, bioinformatics, quantitative molecular biology, statistical genetics, AI/ML, or biostatistics.
- Experience level: Must be relatively new to the application of causality in biomedicine.
- Technical prerequisites: Working knowledge of Linux command line and basic proficiency in R, Python, or Julia.
- Motivation: Clear articulation of how the course will enhance the applicant's research and career.
- Diversity: EMBO encourages applications from underrepresented groups and regions, including LMICs like Nigeria.
Past Winners / Cohort Profiles
The page does not list specific past winners, but typical cohorts include early-career computational biologists, bioinformaticians, and quantitative molecular biologists from European and international labs. Participants often have strong programming skills and a clear biomedical research question that would benefit from causal methods. The course is competitive, with selection based on fit and potential impact.
Ideal Candidate Fingerprint
A PhD student or early postdoc with a solid foundation in programming (R/Python) and Linux, working on a biomedical problem where causal inference would provide a clear advantage over standard association methods. They should have a specific dataset or research question in mind, and be motivated to apply the course's tools immediately upon return to their lab.
Recommended Framing
Eniola should position himself as an independent computational researcher whose Bayesian MCMC-calibrated CCT model for addiction neuroscience already embodies causal reasoning (e.g., estimating causal effects of dopaminergic RPE on reward-memory encoding). He should argue that formal training in causal inference and representation learning will directly enhance his ability to infer causal mechanisms from observational pharmacological data, and that his multi-domain expertise (pharmacology, ML, dynamical systems) uniquely positions him to apply these methods across addiction, drug-resistance, and cardiotoxicity research.
Watch Out
Eniola is not a PhD student or postdoc, but an independent researcher enrolled in a Master's programme. The course targets PhD students, postdocs, and research scientists; his independent status and Master's enrolment may be seen as a mismatch. He should emphasize his research output (preprints, under-review paper) and his role as a de facto research scientist to mitigate this.