Research Associates in Federated and Adversarial Machine Learning
Research Associate to work on EU funded Musketeer project. Musketeer aims to create a federated and privacy preserving machine learning data platform, that is interoperable, efficient and robust against internal and external threats. Led by IBM the project involves 11 academic and industrial partners from 7 countries and will validate its findings in two industrial scenarios in smart manufacturing and health care. Further details about the...
Job listing information
- Reference ENG00916
- Date posted 10 February 2020
- Closing date 10 March 2020
Research Associate to work on EU funded Musketeer project. Musketeer aims to create a federated and privacy preserving machine learning data platform, that is interoperable, efficient and robust against internal and external threats. Led by IBM the project involves 11 academic and industrial partners from 7 countries and will validate its findings in two industrial scenarios in smart manufacturing and health care. Further details about the project can be found at: www.musketeer.eu.
Duties and responsibilities
The main contribution of the RISS group to Musketeer project focuses on the investigation and development of federated machine learning algorithms robust against attacks at training and test time, including the investigation of new poisoning attack and defence strategies, as well as novel mechanisms to generate adversarial examples and mitigate their effects. The work also includes the analysis of scenarios where multiple malicious users collude to manipulate or degrade the performance of federated machine learning systems.
There will be opportunities to collaborate with other researchers and PhD students in the RISS group working on adversarial machine learning and other machine learning applications in the security domain.
To apply for this position, you will need to have a strong machine learning background with proven knowledge and track record in one or more of the following research areas and techniques:
- Adversarial machine learning.
- Robust machine learning.
- Federated or distributed machine learning.
- Deep learning.
- Bayesian inference.
You should have or be close to completion of a PhD degree (or equivalent) in an area pertinent to the subject area, i.e., Computing or Engineering.
You must have excellent verbal and written communication skills, enjoy working in collaboratively and be able to organise your own work with minimal supervision and prioritise work to meet deadlines. Preference will be given to applicants with a proven research record and publications in the relevant areas, including in prestigious machine learning and security journals and conferences.
The post is based in the Department of Computing at Imperial College London on the South Kensington Campus. The post holder will be required to travel occasionally to attend project meetings and to work collaboratively with the project partners.
Should you have any queries regarding the application process please contact Jamie Perrins via firstname.lastname@example.org
Informal Enquiries can be addressed to Professor Emil Lupu (email@example.com)
In addition to completing the online application, you should also include:
- A full CV and list of publications
- A 1 page statement outlining why you think you would be ideal for this post.
Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £34,397 - £37,486 per annum.
For technical issues when applying online please email firstname.lastname@example.org
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Imperial is working in partnership with GIRES to promote respect and provide equal treatment for trans people