Postdoc in Computational mRNA Biology job with Human Technopole | 12799624


Human Technopole

Milan (IT)

to be determined

23 May 2023

22 Jun 2023


Life Science

Job Type


Employment – Hours

Full time


Fixed term





  • 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.

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