IndirizzoJob Profile: Computational Biologist/ Data Scientist
In the National Recovery and Resilience Plan (PNRR), Mission 4 - Component 2 - Line of Intervention 1.4. "From Research to Enterprise," the National Center for HPC, Big Data and Quantum Computing, thematic "Simulations, Computation and Analysis of High Performance Data," facilitation application marked with identification code CN000013,was approved for the realization of the Research Program entitled "National Centre for HPC, Big Data and Quantum Computing"
The IRCCS Humanitas, in according with Directors of the ICSC Foundation law of July 12, 2023 approved the project entitled: " Health Data Platform to unravel the Genetic Complexity to Define Novel Therapeutic Targets in Visceral Cancer" financed by the Innovation Funds (1st tranche); has started the public selection procedures for recruitment under the project entitled: "Health Data Platform to unravel the Genetic Complexity to Define Novel Therapeutic Targets in Visceral Cancer." (code CN000013 National Centre for HPC, Big Data and Quantum Computing)
Project: Unraveling the Genetic Complexity to Define Novel Therapeutic Targets in Visceral Cancer (codice CUP B23D21012250004)
Visceral cancers are cancers that originate in the internal organs of the body, such as the colon, liver, pancreas, and kidneys. These types of cancers are often difficult to detect in their early stages, as they may not cause noticeable symptoms until they have progressed to an advanced stage. To improve patient outcomes, there is a critical need to identify new therapeutic targets and biomarkers for the early diagnosis and prognosis of visceral cancers.
This project will be performed in the Computational Biology research group in according to Prof. Charlotte Ng . The overarching goal is to combine machine learning/artificial intelligence and multiomics profiling of tumors and tumor organoids (PDO) (whole-genome sequencing, RNA-sequencing, proteomics profiling, single-cell RNA-sequencing and spatial transcriptomics) to comprehensively characterize the molecular features of visceral tumors and to define novel therapeutic strategies for visceral cancers.
The person filling this position will focus on:
- The analysis of bulk, single-cell and spatial multiomics data to characterise the cancer-driving alterations of visceral cancers
- The development of machine learning methodologies for the integration of multiomics data to character tumor heterogeneity and define biomarkers
- The development of their own projects within the areas of interest of the laboratories.
Educational qualifications: Candidate must have a PhD degree in genetics, molecular biology, biostatistics, bioinformatics, computational biology or a related discipline
Technical Skills:
- proven experience with next-generation sequencing analysis
- strong computational and statistical skills (e.g. unix, python, R)
- strong interest in cancer biology and machine learning
- ability to work independently, to be a team player, and to collaborate with researchers from various backgrounds
- good organizational skills and ability to handle multiple projects in parallel
- experience with manuscript writing
- fluency in English is a must
Preferential requirements:
- experience with grant writing
- experience with cancer genomics
- background in artificial intelligence and machine learning
Humanitas on the basis of the profiles received, will activate in the terms indicated, the screening process by inviting suitable candidates to participate in the interviews of selection, after verification of the cv and qualifications, which will be held in presence or in call. The recruitment board, will be composed by the Project Manager and the Human Resources Department, will be responsible for the overall evaluation and cv received and will give feedback to the candidates within 60 days of the first interview.
ReOpen Position (RePublished): 04/03/2024 until 24/03/2024
All candidate data collected from the application shall be processed in accordance with applicable law: Dlgs 198/2006 e dei Dlgs 215/2003 e 216/2003; privacy ex artt. 13 e 14 del Reg. UE 2016/679.
