Research Scientist in Secure and Federated AI for Health

at  Springer Nature

Heidelberg, Baden-Württemberg, Germany -

Start DateExpiry DateSalaryPosted OnExperienceSkillsTelecommuteSponsor Visa
Immediate12 Jul, 2024Not Specified13 Apr, 2024N/AChemistry,Molecular Biology,Life Sciences,PhysicsNoNo
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Description:

RESEARCH SCIENTIST IN SECURE AND FEDERATED AI FOR HEALTH

Employer
European Molecular Biology Laboratory (EMBL)
Location
Heidelberg
Salary
Competitive
Closing date
12 May 2024
Discipline
Life Science
Job Type
Researcher
Employment - Hours
Full time
Duration
Fixed term
Qualification
PhD
Sector
Academia
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ABOUT THE TEAM/JOB

EMBL is Europe’s life sciences laboratory – an intergovernmental organisation with more than 110 independent research groups and service teams covering the spectrum of molecular biology. It operates across six sites in Heidelberg (headquarters), Barcelona, Cambridge, Grenoble, Hamburg and Rome. Our mission is to perform basic research in molecular biology; train scientists, students and visitors at all levels; offer vital services to scientists in the public and private sectors within the member states; develop new instruments and methods; and engage actively in technology transfer.
The research group of Oliver Stegle is looking for a highly motivated staff scientist to work on the ELSA project (https://www.elsa-ai.eu) – European Lighthouse on Secure and Safe AI. The successful candidate will be embedded in our research group and will work closely with colleagues at the EMBL Data Science Center and external partners of the ELSA consortium.
Our research group is pioneering computational methods for deciphering molecular variation across individuals, space, and time. We have a track record in the development of computational methods for high-throughput omics data, machine learning for multi-omics integration and causal discovery. In the context of the ELSA consortium, we seek to address pertinent challenges in secure and safe AI for human health. The EC-funded ELSA project has the goal to deliver novel algorithmic solutions for health by bringing the privacy and health communities together. This specific role will focus on the ELSA health use case, which is coordinated by Oliver Stegle and Nvidia.
Our interdisciplinary and international team is jointly located at DKFZ and EMBL Heidelberg, and is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and AI Health Innovation Cluster).

WHAT ELSE YOU NEED TO KNOW

We are Europe’s research laboratory for the life sciences – an intergovernmental organisation performing scientific research in disciplines including molecular biology, physics, chemistry and computer science. We are an international, innovative and interdisciplinary laboratory with more than 1900 employees from many nations, operating across six sites, in Heidelberg (HQ), Barcelona, Hinxton near Cambridge, Hamburg, Grenoble and Rome.
Our mission is to offer vital services in training scientists, students and visitors at all levels; to develop new instruments and methods in the life sciences and actively engage in technology transfer activities, and to integrate European life science research. The working language of the institute is English.
In your online application, you will be asked to include a cover letter and CV.
EMBL is a signatory of DORA. Find out how we implement best practices in research assessment in our recruitment processes here.

Responsibilities:

Your primary task is to foster novel innovations in the field of safe and privacy-preserving AI for health. The role will entail the development and implementation of novel algorithmic solutions, ideally in collaboration with partners of the ELSA consortium. In addition to theoretical and computational development, we also foresee the definition of challenges and relevant benchmarks to be a significant component of the role. The role can also entail interactions with EMBL data services, such as the European Genome-Phenome Archive, where novel federated learning solutions can be implemented in praxis. Engagement in additional measures of ELSA, for example to stimulate interactions between the privacy and health communities would be welcomed.


REQUIREMENT SUMMARY

Min:N/AMax:5.0 year(s)

Information Technology/IT

Pharma / Biotech / Healthcare / Medical / R&D

Software Engineering

Graduate

Computational and statistical development experience with implementing machine learning or statistical learning (e.g

Proficient

1

Heidelberg, Germany