- Milan (IT)
- to be determined
- 23 May 2023
- 22 Jun 2023
- Job Type
- Employment – Hours
- Develop machine learning models for the high-resolution analysis of functional transcriptomics datasets.
- Perform in silico experiments to stress model capabilities in capturing relevant signals under different perturbations.
- Write well-annotated, reproducible code, employing essential tools in computational research, such as version control and dynamic reporting (e.g. git, Jupyter notebooks, Rmarkdown).
- Communicate results to the larger scientific community in the form of scientific manuscripts and conference presentations, with clearly documented methods, results, and visualizations.
- A PhD degree in Computational Biology or related discipline, with a focus on quantitative analysis methods.
- Proven track record of research in Computational Biology, Functional Genomics and/or Machine Learning.
- Background in statistical learning methods, with a focus on interpretable models.
- Strong programming skills in R (Bioconductor) and/or Python.
- Knowledge of next generation library preparation techniques.
- Previous experience with analysis of multiple RNA-seq technologies.
- Experience in developing documented computational tools for -omics data.
- Familiarity with time series analysis and AI approaches (e.g. Natural Language Processing).
- Ability to work independently.
- Willingness to frequently and openly discuss results and ideas, highlighting controls and potential pitfalls, using concise and clear presentations.
- Fluency in English (HT is an international research institute).
- Motivation to interact lively with colleagues in the lab, other departments, and the larger scientific community in various events (journal clubs, lab meetings, seminars, hackathons, international conferences, etc.).
- Strong communication skills and respect for their own and other colleagues’ work, towards fostering a transparent, collaborative, stimulating, and welcoming scientific environment.
- Willingness in identifying third-party funding sources, towards promoting growth into full academic independence.