Research engineer [F/M] for AI-driven molecular and spatial analysis of tumor cell plasticity

Context
A two-year
research engineer
position is available in team BioTiC at Inria Lyon. At BioTiC, we study cellular systems through computational models and bioinformatic data analysis, in order to better understand their organizational principles. In our team, we value working together, being innovative, and providing opportunities for professional and personal growth.
The position is within the framework of project AIMPACT, a collaboration between Magali Richard, Yuna Blum, Sophie Achard (CNRS), Anton Crombach (Inria), and Alex Petersen (Brigham Young University, Utah, USA). The overall goal of this project is to better understand tumor cell plasticity,
that is to say
the ability of cancer cells to dynamically reprogram their transcriptional programs in response to environmental cues.
You will be advised by
Anton Crombach
, whilst working together with other members of the project. Y
ou will be located at Inria’s La Doua site and you will have regular opportunities to visit partners in Grenoble and Rennes.
You will benefit from the expertise at
BioTiC
regarding bioinformatic data analysis and computational modelling of biological systems; and through AIMPACT’s partners you will profit from world-class knowledge regarding statistics, machine learning and AI, and cancer biology. Overall, the project will foster your career in terms of building up cutting-edge expertise on high-dimensional data analysis, network inference, statistics, machine learning and AI, and data visualization techniques – areas that are strongly demanded in academia and industry.
Assignment
AIMPACT
focuses on the identification of molecular mechanisms associated with tumor cell plasticity. Tumor cell plasticity refers to the ability of cancer cells to alter their transcriptional programs in response to environmental cues, enabling them to adapt and evade therapies. Unlike normal cells that have stable cell types, tumor cells can dynamically switch between molecular states, acquiring more aggressive features. This non-genetic adaptability is orchestrated by epigenetic modifications, transcription factor activity, and gene regulatory networks (GRNs). Tumor plasticity has been widely observed across multiple cancers and here we study it in pancreatic ductal adenocarcinoma (PDAC), one of the most lethal malignancies. Despite recent advances, our ability is limited to predict and control tumor plasticity. Addressing this gap requires innovative AI methods that can integrate multi-modal data, incorporate uncertainty, provide interpretable insights, and capture the dynamic nature of cellular state transitions in their spatial context—capabilities that lie beyond conventional approaches.
Your
primary
goal within the project will be to
transform
a prototype software for differential network analysis into a professional Python package.
This will
includ
e,
but
is
not limited to,
developing
novel visualization
features
,
creating
extensive documentation,
preparing
example notebooks,
and identifying candidate regulatory processes for further analysis by AIMPACT’s partners
.
You will also
have time to
pursue
additional initiatives, for instance designing and implementing novel analysis tools that
build on
the existing prototype.
Naturally, w
e are open to discuss other initiatives on a case-by-case basis.
Main activities
The goal of this two-year research engineer position is to
provide and use cutting-edge tools to
better understand how drug tolerance manifests itself in gene regulatory processes. This means your main activities are:
-
transform a prototype software for differential network analysis into a Python package
-
create documentation, prepare example notebooks, design new visualization features
-
apply
differential network analysis
on networks generated by AIMPACT’s partners

• predict genes and genetic interactions linked to drug-tolerance

for further analysis by our partners
Finally, you
are
strongly
encouraged to
disseminate your work through publications in international journals and by presenting it at national and international conferences.
Skills
The project requires skills in computer science, mathematics, and (cancer) biology. A successful candidate has experience in one or more of the following areas: software engineering, statistics and machine learning / AI, explainability,
single-cell data analysis, network inference, data visualization,
programming in Python/R. Affinity with cancer biology and the challenge of cellular state transition in pancreatic cancer is considered a real advantage. Good oral and written communication skills in English are essential.