Postdoc in Predictive Models for Anti-cancer

2 settimane fa

Milano, Italia Springer Nature A tempo pieno

**Postdoc in Predictive Models for Anti-Cancer Therapeutic Target Prioritisation**:

- Employer- Human Technopole- Location- Milan (IT)- Salary- to be determined- Closing date- 25 Feb 2024- Discipline
Life Science
Job Type
Postdoctoral
Employment - Hours
Full time
Duration
Fixed term
Qualification
PhD
Sector
Academia- You need to sign in or create an account to a job.
- Job Details
- Company

**Job Details**:
Job description

**APPLICATION CLOSING DATE: February 25th, 2024**

**About **Human Technopole (HT)**

**Human Technopole (HT)** is an interdisciplinary life science research institute, created and supported by the Italian government, with the aim of developing innovative strategies to improve human health. HT is currently composed of five research centres: Computational Biology, Structural Biology, Genomics, Neurogenomics, and Health Data Science. The Centres work together to enable interdisciplinary research and to create an open, collaborative environment that will help promote life science research both nationally and internationally.

**About **the Iorio Lab**

Situated in the Computational Biology Research Centre of HT**, **the **Iorio **Lab** operates at the intersection of biology, machine learning, statistics, and information theory. Our goal is to comprehend and predict how genomic alterations and molecular traits, derived from various omics, contribute to pathological processes, rewire biological circuits, and influence therapeutic responses in human cancers and other diseases.

Our lab has recently been awarded an **ERC Consolidator Grant (grant n.101125051)**, funding the DepSHOCK project. The project focuses on identifying robust transcriptional markers of cancer cell vulnerabilities. These markers will be used together with single-cell transcriptomic profiling of cancer models, to advance personalised medicine and the rational design of anti-cancer combinatorial therapies.

Leveraging prior knowledge from the _Cancer Dependency Map_, this project will generate post-perturbational transcriptomic datasets at both bulk and single-cell resolution, upon individual CRISPR-based genetic knockouts of relevant cancer dependency genes in associated dependent cancer in vitro models, as well as in a pooled fashion, using scale-limited libraries of single-guide RNAs.

**About the position**

HT benefits from accessibility to the HT National Facility for Genomics, providing state-of-the-art and innovative services ranging from high-throughput sequencing to multi-omics technologies. The HT Data Centre provides a state-of-the-art High-Performance Computing infrastructure and Data Storage support to meet all the computational needs of HT’s research groups.

**Key **Tasks and **Responsibilities**:

- Mining existing cancer dependency map datasets to prioritise cancer dependency genes and corresponding dependent cancer cell lines for inclusion in the DepSHOCK project.
- Computational deconvolution of longitudinal bulk-RNA/proteomic profiles of cancer cell lines upon individual gene knockouts (DepSHOCK footprints).
- Investigating the predictive ability of the DepSHOCK footprints using existing pattern-matching strategies and novel machine intelligence methods.
- Analysis and deconvolution of data from scaled-limited CRISPRscreens with single-cell transcriptomics readouts.
- Designing and using methods to exploit the DepSHOCK data for therapeutic marker discovery and for the design of combinatorial therapies.
- Generating novel scientific hypotheses, interpreting results, writing scientific manuscripts, and disseminating findings through participation in conferences and scientific meetings.

Job requirements

**Key **Tasks and **Responsibilities**:

- Mining existing cancer dependency map datasets to prioritise cancer dependency genes and corresponding dependent cancer cell lines for inclusion in the DepSHOCK project.
- Computational deconvolution of longitudinal bulk-RNA/proteomic profiles of cancer cell lines upon individual gene knockouts (DepSHOCK footprints).
- Investigating the predictive ability of the DepSHOCK footprints using existing pattern-matching strategies and novel machine intelligence methods.
- Analysis and deconvolution of data from scaled-limited CRISPRscreens with single-cell transcriptomics readouts.
- Designing and using methods to exploit the DepSHOCK data for therapeutic marker discovery and for the design of combinatorial therapies.
- Generating novel scientific hypotheses, interpreting results, writing scientific manuscripts, and disseminating findings through participation in conferences and scientific meetings.

**Essential requirements**
- Hands-on experience in data science, preferably in life sciences or medicine.
- Experience in the analysis of genomics or functional-genomics datasets.
- Fluency in English - HT is an international research institute.

**Preferred requirements**
- Experience with the analysis of data from CRISPR-Cas9 screens, siRNA, shRNA, antise

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