Research Scientist - Privacy-Preserving and/or Distributed Learning

Owkin is hiring!

About

OWKIN

At OWKIN, we are dedicated to advancing the state-of-the-art in machine learning for medical research. From finding the next cancer-fighting molecule to tracking tumors, machine learning is an essential tool for pharmacologists, pathologists and many other medical practitioners and researchers. Our team is international, multidisciplinary with incredible talent in machine learning, medicine and business. Our data scientists are among the best in the world, with several Kaggle Masters, top DREAM Challenge performers, and publications in ICML, NIPS and other top scientific journals.

OWKIN’s proprietary platform, OWKIN Socrates, uses machine learning technology to integrate biomedical images, genomics and clinical data to discover biomarkers and mechanisms associated with diseases and treatment outcomes. OWKIN builds scientific collaborations with top-tier medical institutions and partners with leading pharmaceutical companies. OWKIN also develops state-of-the-art federated learning technologies in healthcare to overcome the need to share sensitive medical data, building collective intelligence from distributed data at scale, preserving privacy and security.

Job Description

ROLE

Federated learning is the process through which a predictive model is simultaneously learned across multiple sites, while ensuring that no data, and only very little information overall, leaves each site. At Owkin, we seek to better understand and to improve this process. Our research encompasses many different subjects, ranging from communication-efficient distributed algorithms to privacy-preserving techniques such as differential privacy and secure multi-party computation.

Owkin is a company with a strong academic culture. In our lab, we aim not only to improve our products, but also to develop fundamental research that is aligned with the company's objectives. As such, this is a great opportunity for those looking to combine the freedom of academia to the benefits of working in industry.

The position is located in OWKIN Lab, our Paris-based R&D center. Candidates not meeting every criteria, but who can demonstrate exceptional skill in key areas, are invited to apply.

Preferred Experience

REQUIREMENTS

  • PhD or equivalent degree in Computer Science, Electrical Engineering, Physics, Mathematics, Statistics or any other related field
  • Passionate about doing research and writing papers
  • Publications in one of the following subjects, or other related ones: communication-efficient distributed protocols, distributed learning algorithms, transfer learning, privacy-preserving machine learning, differential privacy, secure multi-party computation
  • Expertise coding in Python, strong grasp of programming best-practices (e.g. code clarity, documentation, git, etc.)

PLUSES

  • 1+ years of experience developing research as a post-doc
  • Has already developed code within a team or open-source project
  • Knowledgeable in machine learning/deep learning concepts and implementation, or interested to learn more
  • Publications in high-impact journals and conferences

YOU...

  • ... enjoy working in a small, passionate team where your contributions will have a direct impact
  • ... value process, standards, communication, and code quality
  • ... want to help build never-before-seen technologies

BENEFITS

  • Competitive remuneration
  • International and high-profile team
  • Regular social events

Recruitment Process

When submitting your CV, please include the following documents along with your personal CV:

  • A one-page cover letter detailing the relationship and possible interactions between your work and federated & privacy-aware machine learning
  • Your full list of publications
  • Two academic references (may be contacted after first interview)

Additional Information

  • Contract Type: Full-Time
  • Location: Paris, France (75003)
  • Education Level: PhD and more
  • Experience: > 2 years