Application Deadline: July 3, 2023
Human Technopole (HT) is a new multidisciplinary life sciences research institute founded and supported by the Italian government with the aim of developing innovative strategies to improve human health. HT consists of his five centers: Computational Biology, Structural Biology, Genomics, Neurogenomics and Health Data Science. These centers work together to create an open and collaborative environment that enables interdisciplinary research and helps promote life science research nationally and internationally.
The Iorio Lab at the Computational Biology Research Center at the Human Technopole in Milan is working on the interface between biology, machine learning, statistics and information theory. Our goal is to explore how genomic alterations and molecular traits from other omics contribute to pathological processes and rewiring of biological circuits, and how they affect therapeutic response in human cancer and other diseases. is to understand and predict what will affect Our research develops algorithms, computational tools, and new analytical methods for the integration and analysis of pharmacogenomics and functional genomics datasets, with the ultimate goal of identifying new therapeutic targets, biomarkers, and opportunities for drug repositioning. It aims to advance human health through design.
We are looking for an ambitious postdoctoral fellow with strong skills in computational biology. Postdoctoral position With a research team led by Francesco Iorio in the Center for Computational Biology. This role will see him actively interacting with other of his HT research centers as well as national and international collaborators involved in the Cancer Dependency Mapping partnership. Its broad goal is to systematically identify cancer vulnerabilities and dependencies for therapeutic use.
Selected candidates will work to develop new algorithms, analytical methods and tools for analyzing data from perturbation screens performed in cancer preclinical models (cell lines, organoids, patient-derived xerographs). Take a variety of phenotypic reads (ranging from cell to cell). to reduced viability, sc-transcriptomics and spatial transcriptomics). We develop machine learning tools for predicting cancer dependence and drug response from the integration of the aforementioned screens and the multidimensional characterization of the screened models. Interact with other research groups and prepare research results for paper submission and presentation at international conferences/conferences.
Main tasks and responsibilities:
- We conduct original computational biology research to develop new methods and models to identify clinically relevant cancer dependencies and vulnerabilities.
- Development and application of new tools for integrated analysis and visualization of large multi-ohmic/multimodal biomedical datasets,
- We design, implement, and describe predictive models based on statistical inference and machine learning approaches to identify molecular determinants of gene essentiality and drug response.
- Contribute to the analysis and interpretation of interdisciplinary collaborative projects with translational and clinical collaborators.
- Contribute to research design and research project management.
job requirements
Mandatory requirements:
- PhD in Computational Biology, Bioinformatics, Statistics, Computer Science, or related fields.
- Strong expertise in quantitative data analysis and visualization.
- Ability to plan, implement and deliver scientific results independently.
- Full proficiency in at least one scripting language (Python, R, Julia).
Priority requirement:
- Experience in developing and implementing statistical and machine learning based predictive models.
- Familiarity with major public sources of transcriptomic and genomic data.
- Excellent oral, written, analytical, and presentation communication skills.
- Good at English.
Other abilities:
- Solid understanding of molecular biology and oncology.
- Previous experience in single-cell omics data analysis, especially single-cell RNAseq.
- Familiarity with cluster computing: Slurm, Nexflow, Snakemake.
- Knowledge of graph databases and graph neural networks.
- Programming Languages: Full scripting ability in both Python and R.
- Python packages: PyTorch, Numba, CUDA.
- Familiarity with software development best practices (testing, version control, CI/CD).
- Excellent publication record in computational biology and bioinformatics journals.
- Experience contributing to the design of interdisciplinary collaborative projects.
Application procedure
Please send the following when applying.
• resume.
• An English motivation letter linking your achievements and call details.
• Names and contact details of the two referees.
Additional Information
HT offers a very supportive and international culture. The working language in HT is English. HT promotes interdisciplinary research of the highest quality by facilitating a vibrant environment made up of highly qualified graduate students, postdoctoral fellows, and independent research groups with access to core facilities .
HT is an inclusive, equal opportunity employer offering attractive terms and benefits befitting a leading internationally competitive research organization, and strives to foster an atmosphere of collegiality and openness. The compensation packages granted are internationally competitive and include pension schemes, health care and other social benefits.
Pursuant to L. 68/99, special consideration will be given to candidates included in the protected category list.
Number of positions offered: 1
contract concluded: CCNL Chimico Farmaceutico, 4 years with tenure – Employee level.
The basis for the position is milan.
