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-driven methods to include prior information about the underlying system Addressing process specific nonlinearities and system-theoretic properties to develop novel control and machine learning algorithms
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generation of enhancement algorithms tailored for endoscopic videos, with a focus on applications in real-time interventions. Development efforts will prioritize both enhancement quality and computational
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underlying system Addressing process specific nonlinearities and system-theoretic properties to develop novel control and machine learning algorithms for edge computing Active collaboration with our industrial
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scientific questions or develop algorithms that perform automatically some challenging tasks. This typically involves exchanging actively with collaborators and domain experts to understand the precise
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bodies in both basic and applied research. Working independently or with other scientific staff to deploy simulation or algorithm pipelines on advanced computational infrastructure, such as High
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, multi-objective optimisation (e.g., genetic algorithms), gait analysis/biomechanics. Proven track record in deploying machine learning models into production (preferred) Proficiency in programming in
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Prof. Marcy Zenobi-Wong (ETH Zürich), and co-supervisors Dr. Marcin Kotlarz (ETH Zürich), Prof. Paul Callaghan (Uppsala University), Dr. Maurizio Gullo (University of Applied Sciences Northwestern
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natural language processing to algorithmically detect hate speech across a variety of online venues (newspaper and social media). To do so, we need an up-to-date, high-quality corpus of training data. Job
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of Applied Sciences (ZHAW), and the Swiss Federal Institute of Technology. The well-established University Department for Geriatric Medicine at the Stadtspital Zurich constitutes a cornerstone of contemporary
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systems. Your work will help to define the quality and features of our algorithms. Armed with your innovative spirit and project experience, you will manifest fresh ideas and novel approaches