Practical courses that attach to the work: research skills for groups and research offices, and team enablement so your people run what we build.
Most technical training fails quietly. People attend, the slides are fine, and three weeks later nothing has changed in the work. We design against that: short cycles, hands-on exercises on realistic data, and a follow-up window for the questions that only appear once people apply the material.
What we teach
The curriculum follows what our own scientists and engineers do professionally, which is the only honest basis for teaching it.
- Data analysis in R and Python. The language and the working habits that keep analysis code readable, reusable and reviewable.
- Bioinformatics. Genomics and metabolomics workflows, from raw data through to interpretation.
- AI tools and deep learning. Including NVIDIA DLI certified courses, which we can deliver in Vietnamese as well as English.
- Research computing. Cluster scheduling, and automation that survives being handed over.
- Team enablement. Training your people to operate and extend the specific systems we have built for you.
How we work
- Needs assessment. We talk to the people who will attend, not only the person who signs off. What can they do now, what do they need to do, and what has been tried before.
- A written plan. Topics, depth, format, duration, and what participants will be able to do at the end.
- Delivery. In person or online, in English or Vietnamese, with exercises on realistic data and, where the material allows, your own.
- Follow-up. A window after the course for real questions, and an honest read on whether the training changed anything.
Attached, not standalone
Training works best bolted onto real work. A course on a system your team is about to inherit lands differently from a course in the abstract, so we would rather bundle enablement into a delivery than sell seats in isolation.
Who this is for
Research groups whose members are learning bioinformatics on the job. University research offices building capacity across departments. Companies adopting GPU computing or new infrastructure who need their engineers productive on it quickly.
