Postdoctoral position in scientific computing in the area of privacy-preserving federated machine learning
Uppsala University is a comprehensive research-intensive university with a strong international standing. Our mission is to pursue top-quality research and education and to interact constructively with society. Our most important assets are all the individuals whose curiosity and dedication make Uppsala University one of Sweden’s most exciting workplaces. Uppsala University has 44.000 students, 7.100 employees and a turnover of SEK 7 billion.
The Department of Information Technology provides education and research of the highest international quality. The department educates roughly 4,000 students each year, and houses about 30 research teams. The strong research focus impacts and provides an excellent foundation for undergraduate education in the department. The department is building on activities in IT that have been carried on at Uppsala University since the mid 1960s. More info: http://www.it.uu.se/.
At the Division of Scientific Computing, we conduct research in the entire chain of what is needed to perform simulations; to mathematically describe the phenomenon under investigation, to formulate a solution method to the mathematical problem, and finally to construct computer programs that efficiently implement the developed solution method to enable the simulation.
You will conduct your research as part of the Distributed computing Applications research group (http://www.it.uu.se/research/group/dca) mentored by Hellander, Toor and Spjuth. You will be expected to actively participate in DCA activities and contribute positively to the general research environment. The DCA research group is an interdisciplinary arena for researchers interested in large-scale distributed and data-intensive computing, data science and computational science and engineering software. The DCA group participates in the eSSENCE strategic collaboration on eScience.
Research Project: Machine learning is a subtopic of artificial intelligence that enables researchers and data scientists to construct algorithms that can learn from and make predictions based on data. In most machine learning workflows today, data is pooled into a centralized dataset that is used to train a predictive model. However, there are many situations in which it is not possible to pool data, such as for regulatory reasons, because the datasets are too large, or because the data is sensitive. In those situations, federated privacy-preserving machine learning allows participating parties to train a joint global model without moving or disclosing any local private data. The project aims at developing new methodology and a technology platform for federated machine learning. Areas of interest include but are not limited to distributed, privacy-preserving optimization, secure multiparty computation, differential privacy and adversarial machine learning. An important part of the project is to push the boundaries for practical use of federated learning, and performance, scalability, and robustness of the developed methods will be important aspects of the research.
Duties: The position is focused on research in the above described research project but may include a limited amount of teaching and departmental duties, but not more than 20%. The work involves traveling to conferences to present result of papers, as well as shorter extended visits to project partners.
Requirements: To qualify for an employment as a postdoctor you must have a PhD degree or a foreign degree equivalent to a PhD degree in in Scientific computing or Computer science, or a for the project relevant area. The PhD degree must have been obtained no more than three years prior to the application deadline. The three year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.
The successful candidate must have documented experience in applied machine learning, optimization and programming. Personal qualities such as dedication, motivation, initiative and independence are valuable. Fluency in spoken and written English is required.
Additional qualifications: Documented experience of research on privacy-preserving machine learning or a closely related area. Cloud computing, data engineering, large-scale distributed machine learning using frameworks such as Apache Spark, Tensorflow, and software engineering.
How to apply: The application should include a research statement (no longer than 5 pages) where the applicant presents a biography (summarizing the doctoral work) and outlines the research proposed to be conducted during the postdoc period in the group. The application should also contain a list of credentials (CV), copies of relevant certificates and grades, a list of publications and contact information to at least three reference persons.
Uppsala University strives to be an inclusive workplace that promotes equal opportunities and attracts qualified candidates who can contribute to the University’s excellence and diversity. We welcome applications from all sections of the community and from people of all backgrounds.
Salary: Individual salary.
Starting date: As soon as possible.
Type of employment: Temporary position according to central collective agreement.
Scope of employment: 100 %
For further information about the position please contact: Associate Professor Andreas Hellander, Andreas.Hellander@it.uu.se.
Please submit your application by 23 September 2019, UFV-PA 2019/2787
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Placement: Department of Information Technology
Type of employment: Full time , Temporary position longer than 6 months
Pay: Fixed salary
Number of positions: 1
Working hours: 100%
County: Uppsala län
Number of reference: UFV-PA 2019/2787
Last application date: 2019-09-23
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