{"id":47084,"date":"2020-06-23T03:00:19","date_gmt":"2020-06-23T01:00:19","guid":{"rendered":"https:\/\/www.cde.ual.es\/?p=47084"},"modified":"2021-11-23T12:59:07","modified_gmt":"2021-11-23T11:59:07","slug":"postdoc-in-robust-machine-learning","status":"publish","type":"post","link":"https:\/\/www.cde.ual.es\/en\/postdoc-in-robust-machine-learning\/","title":{"rendered":"Postdoc in robust machine learning"},"content":{"rendered":"

We invite candidates to apply for one postdoc position funded by The Knut and Alice Wallenberg Foundation through The Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden\u2019s largest ever individual research program, and a major national initiative for strategic basic research, education and faculty recruitment. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society as well as industry.<\/strong><\/p>\n

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This project aims to design and implement robust learning and data-centric optimization techniques for advancing state-of-the-art machine learning algorithms where data is geographically distributed, sensitive, and scarce. Robust machine learning and data-centric optimization algorithms empower models through multi-level (local, global and hybrid) training, learning, and inference with data-centric optimization for scarce data and non-standard model settings. By creating unique features (e.g., decentralized training, learning and inference, fault-tolerant against failures and attacks, data-centric optimization, robustness), this project addresses the challenges in the following areas: robust learning; learning with scarce data and non-standard model settings; lack of theoretical knowledge to build manual models; computation efficient learning and optimization for obtaining more accurate and robust models with applications to constraint environments (i.e., Industrial Internet of Things (IIoT), healthcare systems) and edge infrastructures.<\/p>\n

Qualifications<\/h3>\n

Applicants must have earned a PhD or a foreign degree that is deemed equivalent to a PhD in Artificial Intelligence, Machine Learning or Optimization for Machine Learning, Computer Science or a subject relevant for the position. The PhD degree should not be more than three years old by the application deadline, unless special circumstances exist.<\/p>\n

Since research is conducted in an international research environment, the ability to collaborate and contribute to teamwork, and an excellent command of the English language, both written and spoken, are essential requirements.<\/p>\n

We particularly invite female candidates to apply to ensure gender balance.<\/p>\n

Specific Requirements<\/h3>\n
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Candidates are expected to have outstanding knowledge of machine learning and optimization techniques. Demonstrable knowledge of data wrangling and learning in decentralized settings is a prerequisite. Experience in any of the areas robust learning, fault-tolerant learning and data-centric optimization when data is geographically distributed, sensitive and scarce is a merit.<\/p>\n<\/div>\n

Application<\/h3>\n

The application must be written in English or Swedish. Documents must be in Word or pdf format.<\/p>\n