Improving accuracy and time-to-diagnosis of rare disease by developing AI-based algorithms

RESEARCH PROFILE: First Stage Researcher (R1 )

APPLICATION DEADLINE: 27 June 2021

EU RESEARCH FRAMEWORK PROGRAME: HORIZON 2020

MARIE SKOLODOWSKA CURIE GRANT AGREEMENT NUMBER: 955534

Offer Description

The Innovative Training Network (ITN) “PIPgen - PI3K/PTEN-related monogenic disease to understand cancer" is recruiting 15 highly motivated PhD candidates through an international transparent and open recruitment procedure. The fellowships are funded by the European Commission’s Horizon 2020 programme under the ITN-Marie Skłodowska-Curie grant agreement Nº 955534

More info at:

https://ec.europa.eu/research/mariecurieactions/actions/research-networks_en

About the PIPgen network

The PIPgen network brings together 9 leading European basic and clinical institutions and 3 private companies experts in the PI3K/PTEN- related diseases, to train 15 researchers in a wide range of scientific and complementary competences. Selected candidates will carry out specific projects under the supervision of a Principal Investigator within one of the 11 world-leading European host institutions from the network. They will also perform secondments in other institutions within the network to provide the needed interactions to achieve research and training excellence, and to improve their future career perspectives.

Fellows will be enrolled in a PhD programme and will receive an outstanding and tailored training designed specifically for them. The embedding within the PIPgen network, with experienced trainers from academia and industry and from two research environments (clinical and basic), offers a unique multidisciplinary and multisectoral training opportunity in the field of PI3K/PTEN-related diseases.

Scientific project

PIPgen stems from the emerging links between monogenic rare diseases and cancer, and how these fields can cross-fertilise and inform an integrated approach to both their understanding and treatment. Monogenic diseases offer ‘clean’ molecular, cellular and organismal information about the affected genes, whereas cancer is a compendium of genetic and epigenetic perturbations illustrative of complex diseases. Genetic alterations in the phosphoinositide 3-kinase (PI3K)/PTEN pathway are a common event in both monogenic rare diseases and in cancer, presenting a truly unique paradigm of which PIPgen will take advantage. PIPgen aims to critically contribute by providing a dynamic learning strategy to enhance our understanding of the PI3K/PTEN pathway based on the molecular, biological and clinical integration of both pathological scenarios. PIPgen has been conceived with the view to make a real clinical and therapeutic impact without losing focus on the underpinning basic bioscience.

REQUIREMENTS:

Eligibility criteria:

We welcome applications from PhD candidates from any country fulfilling the following criteria:

• Eligible candidates must not have resided or carried out their main activity (work, studies, etc.) in the country of their host institution for more than 12 months in the 3 years immediately prior to their recruitment by the host institution (i.e. the starting date indicated in the employment contract/equivalent direct contract).
• Eligible candidates shall at the date of recruitment by the host institution (i.e. the starting date indicated in the employment contract/equivalent direct contract), be in the first 4 years (full-time equivalent research experience) of their research careers and not have been awarded a doctoral degree.
• Eligible candidates must have a master’s degree relevant for the chosen position (including biology, medicine, biochemistry, bioinformatics or a related discipline, depending on each PhD project) or its equivalent that would entitle them to a doctorate or must hold an official university qualification from a country of the European Higher Education Area with a minimum of 300 ECTs of official university studies.
• Candidates must have a high level of proficiency in written and spoken English, which will be assessed with the motivation letter and the interview, respectively.
• For applicants of project 3 at Stichting Radboud universitair medisch centrum, in Netherlands, Dutch will be required and epidemiological background will be considered

ADDITIONAL INFORMATION:

Application and selection process

The application will be done through an online application platform to be found on the PIPgen website: www.PIPgen.eu. Applications must be in English. Each applicant may apply to a maximum of three individual research projects.

To apply for the UCL positions, in addition to the procedures outlined here, a further application will need to be completed via the UCL website – this is to conform with UCL’s own recruitment process. This second step is not yet available online, but we will inform candidates as soon as it opens.

Eligible applications will be ranked on the basis of CVs and merits by a selection committee. The 3 best candidates for each position will be invited for a virtual interview by 21-23 July 2021 where the final candidates will be selected.

Applicants with a positive evaluation but not selected will be included on a reserve list to cover eventual future positions and might be contacted at a later stage.  

PhD Project 15: Improving accuracy and time-to-diagnosis of rare disease by developing AI-based algorithms

Next generation sequencing technologies have accomplished the long-awaited milestone of sequencing a genome at a cost below $1000. This makes it possible that millions of people affected by rare diseases can benefit from a diagnostic genetic test. However, once genome or exome sequence is produced, variant annotation, prioritisation and ultimately interpretation in the clinical and familial context, still remains the most important and costly bottleneck. ESR15 will develop a software that incorporates Artificial Intelligence algorithms at different steps and facilitates data interpretation, so at the end, the procedure is faster, more robust, and reliable. ESR15 will develop different machine learning algorithms to improve the process key steps: 1) automation of clinical history gathering into HPO terms, 2) variant categorisation according to ACMG classification, 3) prioritisation of disease-causing mutations, in the scope of the informed phenotype and variants identified.

Host: qGenomics (qG), Spain

Supervisor: Dr. Lluis Armengol

Envisioned secondments: U-Paris,  Amstersdam UMC