Postdoctoral Fellow in Computational Medicine (M/W) (GIF-SUR-YVETTE, FR, 91190)

GIF-SUR-YVETTE, France Type not listed
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Servier is the source of truth for this posting and owns the application process. We surface normalized context and market comparison you won't find on the original listing.

About this opportunity

We are a human-scale, international, and independent pharmaceutical group governed by a Foundation. Our unique model makes us proud and, more importantly, enables us to fully serve our mission: "committed to therapeutic progress for the benefit of patients."

As a global leader in cardiology, we aim to become a focused and innovative player in oncology by 2030, targeting hard-to-treat cancers and dedicating more than 70% of our R&D budget to this goal.

Who are we? 22,000 passionate individuals from over 50 nationalities, driven by an entrepreneurial spirit. Every day, we move forward with and for patients, with and for our teams, motivated by the desire to care, to dare, to grow, and to commit to being useful to those in need.

Come and experience and contribute to our commitment #MovedByYou.

www.servier.com

Are you a dynamic and innovative researcher passionate about advancing cancer treatment through data science? We are seeking a postdoctoral fellow to join our translational medicine research team, focusing on deciphering and modeling therapy resistance. This is a unique opportunity to play a crucial role in understanding intra-tumoral heterogeneity in Acute Myeloid Leukemia patients by applying pioneering AI-based approaches at the intersection of multi-OMIC characterization and disease understanding.

Why Join Us?

Impactful Research: Investigating intra-tumoral heterogeneity is critical to uncovering why treatments fail in AML. By integrating multi-omics data with and without temporal components, you will uncover the molecular and cellular mechanisms underlying disease progression and treatment resistance.

Innovative Environment: Lead efforts in benchmarking and applying AI-driven methods to scRNA-seq analyses. You will have the chance to annotate cell clusters, identify malignant populations, and reconstruct clonal phylogenies to understand evolutionary dynamics across patient response status.

Collaborative Team: Work in an environment that bridges the gap between data science and clinical application, featuring frequent interactions with internal R&D and IT teams to ensure research alignment with broader organizational goals.

Responsibilities:

Self-document on state-of-the-art methods of interest for the project.

Implement, benchmark, and share appropriate methodologies in R or Python for the purpose of the project.

Perform comprehensive multi-omic data analysis to characterize and model relapse-specific tumor clones.

Identify biomarkers or predictive signatures associated with therapy resistance.

Synthesize and interpret analyses in written reports in English.

Share selected project outcomes through scientific publications in peer-reviewed journals, posters, and/or oral presentations at conferences.

Collaborate with cross-functional teams (R&D, IT) to ensure integration of research efforts.

Profile:

PhD in Bioinformatics, Biostatistics, or Data Science, with demonstrated expertise in single‑cell omics data analysis.

Hands‑on experience with single‑cell sequencing technologies (e.g., scRNA‑seq, proteogenomic) and comfort working with complex, high‑dimensional datasets.

Proven experience in developing data science (Machine Learning, AI) applications for translational research projects.

Excellent skills in R and/or Python, including code sharing and versioning.

Proficiency with Linux environments, HPC, and cloud computing.

Good knowledge of molecular biology and proven experience in single-cell data analysis for oncology.

Scientific level English (written and spoken).

Ability to work closely with multidisciplinary teams with good organization and communication skills.

We are committed to equal opportunities and developing talents in all their diversity. We value both experience and the desire to engage daily in contributing to therapeutic progress for the benefit of patients. If this offer resonates with you, seize this opportunity to meet us!

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Job details

Seniority
Intern/Fellow/Postdoc
Function
Drug Discovery & Preclinical Research
Therapeutic area
Not listed
Location
GIF-SUR-YVETTE, France
Employment type
Not listed

How this role compares

Computed from every other active Drug Discovery & Preclinical Research role in our database, not just this employer's listings.

We currently track 59 comparable Intern/Fellow/Postdoc Drug Discovery & Preclinical Research roles across 18 biopharma companies.

59Comparable roles tracked
59Currently active
18Companies hiring similar roles
9Countries represented

Salary context

18 of 59 peers report a salary range (USD, annualized)

Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.

This roleSubject Not listed on this posting
Lowest disclosed · Postdoctoral Researcher in Translational Oncology · Lilly $58,000/yr – $123,200/yr
Highest disclosed · Advisor – In Vivo CAR Discovery · Lilly $138,000/yr – $224,400/yr
Peer group range $90,600 – $181,200 (median $90,600)

Where these roles are based

Top locations among the 59 comparable roles

United States45
Germany5
Belgium2
Switzerland2
Netherlands1
Iceland1

+ 3 more countries

Seniority mix

59 of 59 peers have a known seniority level

Intern/Fellow/Postdoc42
Associate17

Therapeutic area mix

11 of 59 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden

Neuroscience4
Oncology3
Cardiovascular / CVRM2
Immunology1
Respiratory1

Similar opportunities

The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.

45%similar
Lilly Indianapolis, Indiana, United States of America Intern/Fellow/Postdoc
Same function Same seniority
45%similar
Lilly San Diego, California, United States of America Intern/Fellow/Postdoc
Same function Same seniority
45%similar
Lilly Indianapolis, Indiana, United States of America Intern/Fellow/Postdoc
Same function Same seniority
45%similar
Lilly Boston, Massachusetts, United States of America Intern/Fellow/Postdoc
Same function Same seniority
45%similar
Lilly Boulder, Colorado, United States of America Intern/Fellow/Postdoc
Same function Same seniority
45%similar
Lilly Indianapolis, Indiana, United States of America Intern/Fellow/Postdoc
Same function Same seniority

Notify me about similar jobs

Get an email when we spot other openings like this one – same job function, comparable seniority, roles you'd actually want to see.

How we calculate "similar"

No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.

Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.

45%
Postdoctoral Researcher in Translational Oncology
Lilly · Indianapolis, Indiana, United States of America · Intern/Fellow/Postdoc
Function Therapeutic area Seniority Country
45%
QSP postdoctoral scientist
Lilly · Boston, Massachusetts, United States of America · Intern/Fellow/Postdoc
Function Therapeutic area Seniority Country
45%
Post-Doctoral Scientist- Immunology Discovery
Lilly · San Diego, California, United States of America · Intern/Fellow/Postdoc
Function Therapeutic area Seniority Country
45%
Postdoctoral Scientist, Biophysics
Lilly · Indianapolis, Indiana, United States of America · Intern/Fellow/Postdoc
Function Therapeutic area Seniority Country
Unmatched or unknown dimensions score exactly the same: 0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.