Position

Candiolo Cancer Institute is opening a call for a Research Assistant at the Imaging research group of Prof. Daniele Regge (www.aipocratech.com). The research contract will have a duration of 2 years and will be funded by the “ProCancer-I” European Project (reference “952159” – from the call Nº H2020-SC1-FA-DTS-2019-1), granted by the European Union’s Horizon 2020 research and innovation programme.

 

Qualification and expertise

Eligible candidates must hold a PhD or MS degree in either engineering (preferably biomedical, physics or computer) or computer science. Successful applicants should have experience in quantitative medical imaging processing and analysis, and in the development of artificial intelligence algorithms. Basic knowledge of phyton and/or C programming languages and Matlab will be required. Knowledge of ASSEMBLY (x86, ARM/MIPS), JAVA, TYPESCRIPT, and R will be preferrable. Applicant should have good knowledge of spoken and written English language.

 Work plan

Research activities will include:

  1. the development of Deep Learning based models and Radiomics Signatures;
  2. statistical Analysis (i.e., feature selection/reduction, ML modelling using survival analysis and other clinical outcomes).
  3. research on explainable and trustworthy AI

 

Application must include the following:

  • A short letter in which the reasons for application, personal motivation and expectations for the post are explained;
  • Curriculum vitae including personal data, i.e. name, age, master’s degree if any, PhD if any, academic recognitions, awards and prizes, clinical education, previous clinical and scientific positions, publication metrics (Hirsch factor, total number of peer review publications, number of publications during the past five years, as first or last author), editorial work, research supervision, management and financing, teaching experience, number of invited lectures and roles in scientific societies, description of intellectual property if any.
  • List of publications
  • List of meetings attended as an invited speaker
  • Full text of up to 5 publications relevant to the offered position.

 

Primary location of work
Candiolo Cancer Institute and University of Turin. Part of the research might be conducted remotely.

Context

The Aipocratech research group is composed by a multidisciplinary, multi‐investigator team, involving researcher from different fields: medical imaging, computer science, and radiology. The group was founded in 2010 by Prof. Regge, an experienced radiologist and a pioneer in the field of in‐vivo imaging biomarkers. The group significantly contributed to the development of artificial intelligence based systems for colorectal polyps detection at virtual colonscopy, and for segmentation and characterization of breast and prostate cancers on MR images. Ongoing research of the Aipocratech lab include the identification and validation of imaging biomarkers to predict response to therapy.

Applications should be sent at:

Dr. Valentina Giannini, valentina.giannini@unito.it

Prof. Daniele Regge, daniele.regge@unito.it

Highlights

ProCAncer-I at the EMUC24 in Lisbon

ProCAncer-I at the EMUC24 in Lisbon

The progress of major trials, the advent of artificial intelligence (AI), and interdisciplinary best practices will comprise the scientific programme of the 16th European Multidisciplinary Congress on Urological Cancers (EMUC24), which will take place from 7 to 10...

Third Dissemination Event of the ProCAncer-I Project in Athens

Third Dissemination Event of the ProCAncer-I Project in Athens

ProCAncer-I organised the 3rd Dissemination Event of the project at the 21st IEEE International Symposium on Biomedical Imaging, held in Athens, Greece, May 27-30, 2024. During the symposium, ProCAncer-I organised the Workshop “Integrating imaging Data and AI models...

AI in PCa imaging : The current status and future perspectives

AI in PCa imaging : The current status and future perspectives

Ιn recent years, magnetic resonance imaging (MRI) has transformed the prostate cancer (PCa) diagnostic pathway, based on the evidence of multiple high-level evidence studies (refs 4M, MRI first, and PROMIS). Taken together, the evidence indicates that prostate MRI...

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