HIGH confidence
Researched 2026-07-28 13:10 · profile: researcher
Programme Thesis
This programme funds hands-on training on the EMBL HPC cluster environment, covering usage and best practices. It exists to equip researchers with the skills to leverage high-performance computing for large-scale data analysis and simulation in the life sciences.
Selection Criteria
- Eligibility: Open to EMBL staff and external researchers; likely first-come, first-served registration.
- No explicit scoring rubric; emphasis on practical HPC skills (job submission, resource management, parallel computing).
- Reviewer priorities: relevance to applicant's research, prior HPC experience level, and ability to apply training immediately.
Past Winners / Cohort Profiles
No named examples found on page; typical cohort includes early-career researchers (PhD students, postdocs) and bioinformaticians from EMBL and partner institutions seeking to transition to the new cluster environment.
Ideal Candidate Fingerprint
A researcher with basic command-line and scripting skills (Python, Bash) who needs to scale computational workflows (e.g., genomics, molecular dynamics, neurosimulations) to HPC. They should be ready to adopt best practices for job scheduling, data management, and parallel execution.
Recommended Framing
Eniola should emphasize his extensive HPC experience (Nextflow/SLURM, NEURON/Brian2, ODE simulations) and how this training will deepen his expertise on EMBL's specific cluster, directly benefiting his ongoing projects (neurocascade, TOPOLOGIX, ergofluids). His independent, multi-domain profile shows he can immediately apply the training to accelerate his computational neuroscience and pharmacology research.
Watch Out
None; the training is open to external researchers and Eniola's HPC background is a strength, not a disadvantage.