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: Validate the current markerless motion capture system against marker-based data in stroke patients Generate biomechanically accurate synthetic video data to expand training data and enable controlled
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sequencing data quality control within an interdisciplinary setting. This project is particularly suited to candidates with a strong interest in experimental method development and optimisation
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pollutants, a combination of advanced observations, innovative instrumentation and data analysis is required. Recent developments in miniature atmospheric instruments, low-cost air quality monitoring
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modern AI tools, familiarity with proof assistants such as Lean, good communication skills, and ability to work both independently and as a member of a large research team; a good command of English is
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Environment PHYSICAL DEMANDS: Computer data input required. Ability to use computing resources and perform bench-laboratory experiments. WORK ENVIRONMENT:Laboratory environment. Universal precautions
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Join an exciting European research initiative shaping the future of sustainable electronic systems. Job description The successful candidate will conduct research within the EU-funded E2Packman
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-preserving, single-camera markerless motion capture for clinical assessment and long-term home monitoring of motor impairments in stroke and Parkinson's disease patients. The position is based at TU Delft
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Analysis Ability to conduct research work independently and systematically in a collaborative environment. Drive and ability to collaborate Assessment criteria This is a career development position primarily
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research skills and the ability to work independently. Experience with Python is considered an asset. You have an excellent command of spoken and written English. You are a team player with strong
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Athletic Director’s staff and to help manage a large cohort of student analysts (approximately 40 undergraduate and master’s students). The appointment is renewable annually for up to a total of three years