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Postdoc openings on mathematical modeling and AI in drug resistance in melanoma

  • 15 May 2021
  • Montpellier and Paris, France

Job details

2 postdoc positions are available within the ITMO Cancer project MALMO (Mathematical Approaches to Modelling Metabolic Plasticity and Heterogeneity in Melanoma) on mathematical oncology and explainable AI

MALMO is financed by the MIC program of Itmo Cancer from November 1, 2020 until October 31, 2023.

1st position : mathematical modeling of melanoma resistance in space and time 

Department: Laboratory of Pathogen Host Interactions, LPHI UMR 5235 CNRS, University of Montpellier, Montpellier, France

Supervisor and contact: Ovidiu Radulescu: ovidiu.radulescu@umontpellier.fr

Start: July 1, 2021

Duration: 24 months

Qualification: candidates should have a PhD in Applied Mathematics, Biomathematics, Computer Science, or any other field related to the project.

Deadline: candidates should send CV, motivation letter and names of referees to the supervisor and also apply on the CNRS website https://emploi.cnrs.fr/ preferably by May 15, no later than May 31, 2021.

2nd position: explainable AI approaches for tumor heterogeneity and genesis study, modeling and analysis

 Department: Institut du Cerveau - Paris Brain Institute - ICM, Paris, France 

CNRS UMR 7225, Inserm U 1127, AP-HP, Hôpital de la Pitié Salpêtrière, Sorbonne

Supervisor and contact: Daniel Racoceanu, Prof. Sorbonne university and PI @ ICM: daniel.racoceanu@sorbonne-universite.fr

Start: July 1, 2021

Duration: 24 months

Qualification: candidate should have a PhD in Computer Science with particular focus on Machine Learning methodologies (experience with Deep Learning), BioMedical (but not only) Image analysis and Mathematical modeling.

Application deadline: applicants should contact the supervisor preferably by May 15, no later than May 31, 2021.

Context of the project:
CNRS Occitanie Est and Paris Brain Institute are recruiting two postdoctoral fellows from applied mathematics, biomathematics, computer science, machine learning, biomedical image analysis to take part in the MALMO project which is supported by the ITMO Cancer and Montpellier University. The aim of these scholarships are: 1) to develop mathematical models describing cellular heterogeneity of melanoma tumours and its evolution under treatment; 2) to use AI approaches for learning models initial conditions and environmental constrains such as blood vessels distribution and nutrient/oxygen gradients; 3) to validate the models by using multiplexed tissue imaging data and digital pathology images produced in this project. The two postdoctoral fellows will collaborate tightly with each other, with their respective supervisors (O. Radulescu and D. Racoceanu) and the other members of our consortium, including experimental biologists and clinicians (team of Laurent Le Cam at Montpellier Cancer Center – IRCM).

The contributions expected from these positions include: development of high dimensional PDE models and of numerical solutions well adapted to high dimensions, development and implementation of model order reduction methods, practical and foundational results in hybrid AI and image analysis for oncology.  

The fellows will have the opportunity to enhance their expertise by embarking in an interdisciplinary project developing cutting edge spatially resolved, multiplexed tumour imaging and digital pathology techniques, new mathematical frameworks for multiscale modelling and hybrid (generative – discriminative) IA approaches.

Our hiring process strives for equal opportunities. We are fully committed to equality of treatment for all candidates.


1st Pos-Doc position (Mathematical modeling): LPHI is an innovative and high standard laboratory for basic research in biology. It host the Computational Systems Biology Team (CSBT), one of few of this kind in France, developing projects at the interface between Biology, Physics and Mathematics. Team leader of CSBT, Ovidiu Radulescu is with University of Montpellier. Established in 1289, the University of Montpellier (UM) is the 6th largest university in France, with about 50,000 students including 7000 come from abroad to study in Montpellier. One of the most innovative higher education institutions in the world, UM ranks very high in many international rankings : first in the world in the 2018 Shanghai ranking for Ecology, first most innovative French university in 2018 Reuter’s ranking, 5th in France in 2018 Leiden’s ranking for the quality of its scientific publications, 3rd French university in the 2019 “University Impact ranking” of Times Higher Education. These increasingly outstanding results reflect the dynamism triggered by the Montpellier University of Excellence I-SITE project since the prestigious certification was obtained in March 2017.

Montpellier is a vibrant and sunny Southern France city. It benefits of the Mediterranean coast and proximity of the Cevennes mountain range, has a beautiful old city centre and great infrastructure.

2nd Post-Doc position (IA-ML/DL) : Paris Brain Institute (PBI / ICM), Paris, France

Paris Brain Institute (PBI / ICM - CNRS, Inserm, Sorbonne, AP-HP) - INRIA team « Aramis ».

Located within the Pitié-Salpêtrière hospital (major European hospital), PBI is an international research center whose innovative concept and structure make it unique. The best scientists from all backgrounds and countries come together at the Institute to perform leading-edge research in this area.

Daniel Racoceanu is a principal investigator and professor at Sorbonne University, a multidisciplinary, research-intensive, world-class university. Located in the heart of Paris, with a regional presence, it is committed to the success of its students and to meeting the scientific challenges of the 21st century. Thanks to its 55,300 students, 6,400 academic researchers and partner researchers, and 3,600 administrative and technical staff who make it a daily reality, Sorbonne University promotes diversity, creativity, innovation and openness to the world.

Biological background
etabolic rewiring is a recognized hallmark of cancer cells.  The metabolic reprogramming of transformed cells is required to sustain proliferation and biomass production, but it also influences many other biological processes, including cell signalling or the epigenetic control of gene expression. Many metabolic alterations of cancer cells influence their sensitivity to chemo- and targeted therapies but the underlying mechanisms are not fully understood. Directly relevant to our project, it is well recognized that his metabolic reprogramming is influenced by variable microenvironmental conditions, including nutrient and oxygen availability. Our objective is to further understand cancer heterogeneity in melanoma from a metabolic standpoint and to evaluate how gradients of nutrients and oxygen influence melanoma cell fate and drug resistance.

Mathematical modelling background
Cancer is a complex disease involving multiple genetic and epigenetic changes that continuously evolve during disease progression. In order to survive and proliferate, cancer cell populations use adaptive evolution strategies based on heterogeneity and survival of the fittest cells. The strong plasticity of cancer cells leads to the rewiring of signaling pathways and metabolic networks, all in response to changes of their micro-environmental conditions. For all these reasons, mathematical modeling of cancer evolution should include several biological scales: molecular, cellular and tissular. In this project we represent cancer cells as distributions over spatial and multiple metabolic dimensions and study their evolution using high dimensional partial differential equations models.

Machine learning and image analysis background
In this project, various AI - including machine and deep learning - methods will be used to quantify the tumor spatial heterogeneity at different scales (distribution of blood vessels, invasion fronts, cell clusters and distribution of metabolic markers) from H&E-stained histological sections and from mass cytometry tissue imaging datasets. Ultimately, these methods will feed the mathematical model with information (initial data and parameters) needed for predicting tumor evolution upon targeted therapy. A particular accent will be set on the explicability of the innovative AI approaches, allowing us to develop a creative framework, by involving our biomedical partners).

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