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Human Technopole is taking the first steps towards establishing a unique large-scale scientific infrastructure of great socio-economic and cultural impact. An international collaborative inclusive dynamic community of highly motivated talented and creative people will make the difference.

If you are a passionate person who likes to seize great challenges, please don’t hesitate to apply. Transparent and merit-based selection methods will drive the recruitment and hiring process. The recruitment of Senior Scientific Staff will be conducted exclusively via international call, according to international standard.


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HT Job Viewer Portlet

  • Post Doc
  • Computational Biology
  • Fri, Feb 28 2020
  • Applications have closed


Mon, Feb 10 2020

The Human Technopole Foundation is seeking a highly motivated researcher with strong skills in Computational Biology to fill a Postdoctoral fellow position.


The aim of the fellow is to develop new algorithms and computational tools for the analysis of cancer pharmacogenomics and functional-genomics datasets (from the characterization of in-vitro cancer models, organoids and patient derived xenografts) generated by collaborators at Candiolo IRCCS (Turin, Italy) and the Wellcome Sanger Institute (Hinxton, UK) to identify new oncology therapeutic targets and markers of gene-essentiality/drug-response.


To achieve this, the successful candidate will design methods to transform raw data into interpretable and predictive models via systematic statistical inference and machine learning approaches. Additionally, she/he will integrate data from large scale in-vitro drug/genome-editing screens with the multi-modal characterization of the underlying models and that of cancer patients (from publicly available resources) in order to:


  1. optimize/identify the most clinically relevant molecular determinants of gene-essentiality/drug-response;
  2. prioritize potential new targets/therapeutic-markers on the basis of unmet clinical needs and translational potential;
  3. adaptively account for possible genomic drifts resulting from multiple passages of the in vitro models under study while modeling corresponding pharmacogenomic data.


The selected candidate will join the research group led by Francesco Iorio within the Centre for Computational Biology. She/he will actively interact with collaborators form the international Cancer Dependency Map partnership, whose goal is to identify vulnerabilities and dependencies that could be exploited therapeutically in every cancer cell to advance personalized cancer treatments. The groups involved in this initiative have already characterized up to 1,000 cancer cell lines using high throughput genomic, transcriptomic, and proteomic techniques, as well as applied large scale drug panels to assess cell-line specific sensitivities. Work is currently ongoing to identify genes that inhibit cell growth when knocked out using CRISPR/Cas9 -- these could then be used as targets for therapies.


The successful candidate will perform original research to globally advance the state of the field with novel methods, data resources and results. She/he will as well apply methods and to gain new insight into cancer dependencies.


In addition, she/he work toward the identification of solutions and the development of in-silico correction methods for genome-editing technology-specific problems and for the optimisation and the design of RNA guide libraries.


Essential Skills


· Motivation to understand what makes a gene a good therapeutic target in cancer, interest in science, ability to get things done;

· PhD in a relevant subject area (Physics, Mathematics, Computer Science, Engineering, Statistics, Computational Biology, Bioinformatics, Molecular Biology);

· Ability to devise novel computational methods;

· Good knowledge of statistics, combinatorics;

· Good knowledge of algorithms and data structure;

· Familiarity with machine-learning and descriptive statistics;

· Full working proficiency in a scripting language (e.g. R, Python, Perl), and UNIX/Linux;

· Ability to work independently, organise workload, and communicate ideas and results;

· Fluency in English – HT is an international research institute;

· Good communication skills;

  • Ability to work in a multi-cultural, multi-ethnic environment with sensitivity and respect for diversity;
  • Ability to build trust through operating with transparency and creating an open and positive environment.


Ideal Skills


· Basic Knowledge of genomics, molecular biology, and cancer genetics;

· Basic Knowledge of machine learning, information theory;

· Previous experience with genetic screens;

· Previous experience with high throughput biological assay analysis;

· Previous experience in creating finished software;

· Full working proficiency in a compiled language (e.g. C, C++, D, Julia, Fortran);

· Previous experience with implementing–omics data analysis pipelines on a cluster;

· Proven independent working style, problem solving, data analysis and generation of novel ideas;

· Strong publishing record;

· Excellent ability in delivering scientific talks and good paper writing skills.



HT offers a highly collaborative, international culture. The working language at HT is English. HT will foster top quality, interdisciplinary research by promoting a vibrant environment consisting of independent research groups with access to outstanding graduate students, postdoctoral fellows and core facilities.


HT is an inclusive, equal opportunity employer offering attractive conditions and benefits appropriate to a leading, internationally competitive, research organisation and seeks to promote a collegial and open atmosphere. The compensation package granted will be internationally competitive and comprise pension scheme, medical and other social benefits and support for relocation and installation.


The selected candidate will be offered a 3yrs fixed term position with a gross annual salary ranging from 32K to 38K euros (based on experience).


Please apply sending a CV and motivation letter in English only through the dedicated area below.

For specific enquires concerning the role only, please contact Dr. Francesco Iorio: (this email address should not be used to send applications).