Postdocs in Computational Biology – Testa Group

Human Technopole (HT) is an interdisciplinary research institute in Milan (Italy) created and supported by the Italian government to develop innovative strategies to promote human health. HT adopts a multidisciplinary and integrated approach combining genomics, computational and structural biology, neuroscience, and data and decision sciences.

The Human Technopole Foundation is looking for two highly motivated postdocs to work in the group of Prof. Giuseppe Testa, Neurogenomics Research Centre in the areas of artificial intelligence and bioinformatics. We are seeking outstanding candidates with excellent problem-solving, analytical and critical thinking skills who are passionate about the challenges of quantitative approaches to advance human neurobiology and make an impact in neuropsychiatric disorders:

• Artificial Intelligence and mathematical modelling, with key edges in pattern finding       across diverse data and multiple observables across scales

• Multi-omics bioinformatic analysis, with a strong focus on integrative data analysis to pursue mechanistic convergences and actionable hubs for clinical translation

About the lab:

We focus on the dynamics of brain disorders, with a strong emphasis on human experimental models and straddling multiple scales of analysis from single cell resolution to organismal function, working in close integration with national and international deeply phenotyped cohorts that provide unique edges for the study of gene/environment interactions in mental health vulnerability and resilience.  Our research environment includes transformative facilities encompassing reprogramming, editing and brain organoids automation, state of the art genomics and microscopy infrastructures along with a HT-wide architecture of high performance computing (HPC) attuned to the needs of contemporary computational biology.

See previous work on the research topics of this call:

Caporale, N. et al. Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution. Nature Methods 1–13 (2024).

Bosone, C. et al. A polarized FGF8 source specifies frontotemporal signatures in spatially oriented cell populations of cortical assembloids. Nature Methods 1–13 (2024).

He, Z. et al. An integrated transcriptomic cell atlas of human neural organoids. Nature 635, 690–698 (2024).

López-Tobón, A. et al. GTF2I dosage regulates neuronal differentiation and social behavior in 7q11.23 neurodevelopmental disorders. Science Advances 9, eadh2726 (2023).

Caporale, N. et al. From cohorts to molecules: Adverse impacts of endocrine disrupting mixtures. Science 375, eabe8244 (2022).

Cheroni, C. et al. Benchmarking brain organoid recapitulation of fetal corticogenesis. Transl. Psychiatry 12, 520 (2022).

Key tasks and responsibilities

• Mathematical and computational modelling to study neurodevelopmental dynamics at the single cell level with cutting-edge AI methods. Analyse and interpret complex empirical data from stem cell and organoids based disease modelling experiments and connect the molecular and clinical phenotypes‘ to study neurodevelopmental dynamics at the single cell level.

• Develop predictive models from empirical data leveraging genetic and/or pharmacological multiplexed perturbations

• Analyse and integrate high-content microscopy data with single cell multi-omics of complex organoids models. Work alongside experimental biologists to design and formulate innovative hypothesis aiming at high resolution understanding of neurodevelopmental disorders Establishing reproducible and well-documented bioinformatic pipelines for the standardised analysis of internal and external single cell multi-omics datasets. Supporting researchers and collaborators by performing bioinformatic analysis and providing advice on the best suited statistical models. Strongly interact with wet and dry scientists of the lab. Communicating, explaining and contextualising computational results to a wide variety of collaborators with varying degrees of know-how. Maintaining and documenting software, following best practices of software engineering (e.g., revisioning and containerization).

Job requirements

Mandatory:

• A PhD degree in a quantitative field (bioinformatics, computer science, statistics, mathematics, physics, engineering or similar). Proven and extensive knowledge of at least one of the following programming languages: Python (preferable), R, java, javascript, C++, C#; Experience in machine learning. Previous experience in the analysis of single cell omics datasets and an interest in neurobiology (or previous experience in the field); Fluency in English – HT is an international research institute;

Preferred:

• Proficiency with code versioning systems (e.g., git, SVN) and familiarity with container technologies (e.g., Docker, Singularity, Podman); experience working in a Unix/Linux environment; Experience in machine learning for omics data. Experience in bioinformatics workflow systems (i.e., Nextflow, Snakemake, CWL);

Soft skills:

• Strong organizational skills and attention to detail; capacity to structure analytical steps in well-documented and reproducible workflows; Collaborative attitude and ability to work in teams with colleagues of heterogeneous scientific backgrounds; Ability to manage competing priorities in a fast-paced environment and to work independently;

Application Instructions

Please apply online by sending:

i) a CV

ii) a motivation letter in English

iii) names and contacts of 2 referees

For any inquiry about the call, please feel free to contact Prof. Giuseppe Testa, giuseppe.testa@fht.org (this email address should not be used to send applications).