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physiological conditions concerning health and performance. Your tasks Develop data processing pipelines for multimodal physiological signals (pre-processing, feature extrac-tion, and data fusion) from wearable
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master’s degree in physics, materials science, electrical engineering or a related discipline with hands-on experience in thin film deposition and UHV technology. Prior experience or knowledge in
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. Empa is a research institution of the ETH Domain. To strengthen our team and enhance our knowledge and understanding in pyrolysis processes we are looking for a PhD student for scientific analysis
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talented candidate with a Master’s degree in civil engineering, mechanical engineering, or a related discipline Solid background in mechanics of materials and finite element analysis Sound knowledge
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- or nanoplastics, online or offline ice nucleation experiments. A good knowledge of programming languages such as Python, R, MATLAB or IGOR is expected. Excellent English skills, both in verbal and written
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multidisciplinary thinking & strong motivation and interest to join an ambitious interdisciplinary research team with integrative and cooperative personality. Excellent English knowledge (written and oral) is
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-related transport phenomena all require precise knowledge of fluid flow dynamics. Advanced experimental methods such as Particle Image Velocimetry (PIV) and 3D Lagrangian Particle Tracking (LPT) provide
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generate new scientific knowledge at the forefront of materials science, machine learning, and catalysis. This is an exciting opportunity to tackle a fundamental scientific challenge while developing skills
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related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy
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both independently and in interdisciplinary research teams. Proficiency in English (spoken and written) is required; knowledge of German and/or French is an advantage. Our offer We offer a challenging