About the role:

At Waiv, we build high-performance diagnosis and prognosis models from digital histopathology whole slides for cancer patients. These models are at the core of our diagnostic products such as RlapsRiskⓇBC and MSIntuitⓇCRC. For real-world clinical deployment, robustness to pre-analytical and technical variability - such as staining differences and scanner heterogeneity - is critical. While robustness can be addressed at the foundation model (FM) level through improved tile representations, experience shows that this is not sufficient for clinical-grade performance. In practice, additional robustification strategies are required at the downstream stage - particularly during multiple instance learning (MIL) training - as well as calibration procedures to optimize decision thresholds for specific deployment cohorts.

This internship aims to enhance the robustness of our biomarker prediction pipelines through tailored data augmentation (e.g., [1]) and downstream aggregation methods (e.g., [2]) , with the optimal goal of removing post-hoc, center-specific calibration. During this internship, your work will directly contribute to improving Waiv’s diagnostic and internal pipelines to build more reliable diagnostic tools.

In particular, you will:

• Collaborate closely with, and receive mentorship from, the other members of the team;

• Conduct primary research and numerical validation on your topic of study, including re-usable software implementations;

• Contribute to regular research review;

• Report your detailed findings to the group.

• This position is primarily based in Paris, France. Ideally you are able to join the team in March/April 2026.

References

[1] Boutaj, S., Scalbert, M., Marza, P., Couzinie-Devy, F., Vakalopoulou, M., & Christodoulidis, S. (2025). Controllable latent space augmentation for digital pathology (arXiv:2508.14588) [Preprint]. arXiv. https://arxiv.org/abs/2508.14588

[2] Lin, S.-Y., et al. (2025). Contrastive learning enhances fairness in pathology artificial intelligence systems. Cell Reports Medicine, 6(12), 102527. https://doi.org/10.1016/j.xcrm.2025.102527

About you

Required qualifications / experience:

• You are enrolled in a master degree in mathematics, statistics, biomedical engineering, computer science or related field

• Authorization to work legally in France

• Fluent in English (spoken and written)

• Proficient in Python and in relevant librairies (Numpy, Pandas, PyTorch, TensorFlow)

• Strong understanding of deep learning concepts and algorithms

• Previous experience in applying deep learning algorithms to real-word data

• Interest in medical imaging applications

• Strong communication skills

• Good team player

Optional Qualifications/Experience:

• Prior knowledge of generative adversarial networks and self-supervised learning.

• Experience training deep-learning models in a distributed environment.

• Familiarity with the SLURM container orchestration tool.

Recruitment Process & Security

• Please attach a CV in English.

• Waiv is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, sex, gender, sexual orientation, age, color, religion, national origin, protected veteran status or on the basis of disability.

• Waiv is a great place to work. Unfortunately, being a coveted workplace means we are vulnerable to recruitment phishing scams. We urge all job seekers and candidates to be wary of potential scams. Most of these have individuals posing as representatives of prominent companies, including Waiv, with the aim of obtaining personal, sensitive, or financial information from applicants. These scams prey upon an individual’s desire to obtain a job and can sometimes “feel” like a genuine recruitment process. Some red flags are identified below. Should you encounter a recruitment process that claims to be for Waiv but is not consistent with the below, please do not provide any personal or financial information:

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