Distributed Machine Learning including, but not limited to the Federated Learning (FL) paradigm, is opening new possibilities for the training of large scale machine learning models using data held by different clients in a trustworthy, privacy-preserving manner. Of course, FL is not without its challenges, including practical scalability aspects, issues related to heterogeneity of processing and network capacities, economics and fairness aspects, as well as security and privacy threats from malicious clients and servers. The purpose of the position will be to tackle such challenges, investigate synergies with cloud and edge computing, as well as develop practical use-cases of distributed ML for concrete examples coming from finance, advertising, e-health, transportations, etc. The focus will be on algorithms and systems aspects of the proposed solutions and will also include practical demonstrators for real-world problems from the aforementioned domains.

• Degree in Computer Science, Physics, Mathematics or related technical field.
• Strong background on developing distributed systems.
• Strong background on machine learning and algorithms.
• Knowledge on security and privacy.
• Proficiency in written and spoken English is mandatory.
• A desire to work on challenging topics and publish at top conferences is also mandatory.
• Strong publication record.
• Experience in managing and writing research grants.
• Experience in mentoring younger researchers.

 

IMDEA Networks Institute aims to increase the proportion of women and therefore qualified female applicants are explicitly encouraged to apply. Until a balanced ratio of men and women has been achieved at the institute, preference will be given to women if applicants have similar qualifications. IMDEA Networks Institute actively promotes diversity and equal opportunities. Applicants are not to be discriminated against in personnel selection procedures on the grounds of gender, ethnicity, religion or ideology, age, sexual orientation (anti-discrimination). People with disabilities who have the relevant qualifications are expressly invited to apply.

This position could be co-financed with the project MLEDGE: “Aprendizaje automático en la nube y en el borde (Cloud and Edge Machine Learning)”, with reference No. EGAGE22e00052829516, funded by the Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the Spanish Recovery, Transformation and Resilience Plan (PRTR).