PhD Position F/ M Mechanistic learning of the natural history of lung cancer
Publicerad 2024-07-01
Contexte et atouts du poste
This PhD position will take place in the environment of the Inria-Inserm team COMPO (COMputational Pharmacology in Oncology), located in the La Timone health campus. The team is composed of mathematicians, data scientists, pharmacists and clinicians and is a unique multidisciplinary environment focused on developing novel computational tools for decision-making in clinical oncology.
The PhD student will join a national consortium in the context of the LUCA-pi (lung cancer prevention and intervention) national RHU project (30M€ with 10M€ from the French national research agency) consisting of:
AP-HM (univerty hospitals of Marseille, Pr D. Boulate) Gustave Roussy Institute (Pr L. Zitvogel and Pr G. Kroemer) Center for Immunology of Marseille (P. Milpied) Inria – Inserm COMPO The objective is to develop a mechanistic mathematical model of the lung cancer national history and combine it with machine learning algorithms to predict a localized, early-stage lung cancer or the post-surgery metastatic relapse. The PhD will be supervised by a mathematician/data scientist (Dr S. Benzekry, head of COMPO) and a thoracic surgeon (Pr D. Boulate, PI of the LUCA-pi project).
Mission confiée
Data
The project builds on already existing databases and ongoing prospective projects integrating high dimension clinical, imaging and biological longitudinal phenotyping. The PREVALUNG, PREVALUNG ETOILE and PREVALUNG BIOCEPTION are 3 intertwined projects respectively funded by the National Institute against cancer (INCa), Aix-Marseille University Fundation for Excellence (A*midex) and the European commission (PREVAUNG EU, Horizon Europe Program). The PREVALUNG studies are recruiting 2750 participants with 3 rounds of lung cancer screening including baseline and longitudinal multimonal phenotyping.
Principales activités
Main activities:
Review of the literature Benchmark of existing methods Development of novel "mechanistic learning" algorithms Interactions with the biological and clinical partners Writing scientific publications Additional activities: Continuous integration / continuous deployment of the code Data visualization Statistical reporting to the partners
Compétences
Technical skills and level required :
Excellent programming skills in a scripting language (R and/or Python) Strong background in statistics and machine learning Hands-on experience with real-world data analysis Ideally, experience in mixed-effects modeling Experience in computer vision is a plus Strong motivation for medical and societal applications of computational methods Knowledge of biology and/or medicine is a plus Ability to work both independently and as a team, good relational skills Additional:
English speaking Intermediate academic writing skills Intermediate oral presentation skills
Avantages
Subsidized meals Partial reimbursement of public transport costs Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.) Possibility of teleworking and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Contribution to mutual insurance (subject to conditions)
Rémunération
Gross Salary per month: 2100€ brut per month (year 1 & 2) and 2190€ brut per month (year 3)
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