174 computational-physics-"https:"-"https:"-"https:"-"https:"-"FCiências" positions at ETH Zurich
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extension depending on programme needs and performance. Job description As the Admissions & Recruitment Manager, you will play a key role in managing the admissions process, organising recruitment activities
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Contribute to supervision of students and interns where appropriate Profile PhD in climate science, atmospheric science, computer science, data science, physics, applied mathematics, remote sensing, or a
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Computer Vision and Computer Graphics techniques to digitize human avatars and garments in 3D. Within this project, your role is to advance our existing algorithms that reconstruct 3D garments from multi
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100%, Locarno, fixed-term Recent advances in AI-based weather prediction have demonstrated remarkable skill and computational efficiency. However, most current machine-learning weather prediction
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could have impact on diseases with inflammatory and metabolic components. Job description As a PhD student, you will: perform biological and physical characterization of the glycopolymers in different
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environmental assessment, as well as policy and regulation. Job description The doctorates will be conducted within the Einstein School’s doctoral program. The positions are for three years, starting in September
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operations that are yet to be fully understood. In this context, it is evident that the operation, control, and planning of power systems will soon be pushed to their limits. Therefore, new computational
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computational workflows to design novel AAV capsids Dry-to-wet: lead the computational design process and actively participate in wet lab validation of your designs (e.g., library construction, viral production
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datasets The position is limited to two years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep
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years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep-learning models on distributed systems