Industrial PhD Fellowship in Generalized Supervised Classification

Topics: Generalized supervision for scenarios affected by noisy labels, semi-supervision, domain adaptation, and heterogeneous samples.

Requirements:
• Promising young researchers. •Applicants should have their MSc. completed before 31.11.2020. MSc. degree is required from mathematics, computer science or related area.

Skills and track-record:
• Ability to effectively communicate and present research ideas.
• Previous experience in research projects is highly desirable.
• High level of spoken and written English.
• Good communication and interpersonal skills.

Scientific Profile: The researcher will develop techniques for supervised classification that aggregate general ensembles of training samples with different types. The thesis will first develop techniques for scenarios affected by noisy labels and semi-supervision. Finally, the thesis will develop techniques for scenarios with more general training samples such as those developed under the paradigms of domain adaptation and transfer learning. Candidates are expected to have strong mathematical background with experience on software languages for numerical computing such as Matlab and Python. Other fields relevant for the Thesis project are: convex optimization, linear algebra, and statistics.

Deadline: 30th September 2020, 14:00 CET (UTC+1)

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