- Contract duration: 1 year
- Grading: 5 or 6 (monthly salary starting from 3,5k EUR after tax + other paid benefits)
- Reference number: HD02585
- A PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical development
- Experience with implementing machine learning or statistical learning (e.g. Bayesian inference, deep-learning), ideally connected to privacy or federated learning
- Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro or TensorFlow
- The Motivation and Dedication to lead a scientific project and you are a supportive, creative and responsible team member.
- Experience in health research or genomics are beneficial but not required. The post is equally suited for a scientist with a background in AI for health or with a background in AI for privacy and/or federated learning.
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Research Scientist in Secure and Federated AI for Health - Heidelberg, Deutschland - Embl-Ebi
Beschreibung
Your role
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.
Closing date: 12 May 2024
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