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field A strong background and keen interest in uncertainty quantification, structural reliability, optimization, Bayesian inference, surrogate modeling/emulation, and/or machine learning Excellent
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Excellent programming skills (preferably Matlab, or Python), and some experience in machine learning Strong analytical and problem-solving abilities Excellent communication and scientific writing skills, and
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, Information Technology, Information Systems, Statistics, Data Science, Engineering, or a related field Strong machine learning and programming experience with the ability to work across frontend, backend, and embedded
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Location: Sofia, Sofia 1784, Switzerland [map ] Subject Areas: Computer Science / All areas Quantum Computing / Quantum Computing Artificial Intelligence Machine Learning / Machine Learning Natural
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upcoming areas off the beaten paths. Our three main areas of research are machine learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects
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Technical Proficiency: Familiarity with engineering tools with focus on design automation Experience in programming (e.g. Python) or machine learning and a desire to deepen your expertise Experience in
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. Interactions with the recently created Dubochet Center for Imaging are highly encouraged. One or two of the following research topics should be covered in the application: Machine Learning and AI-inspired
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willingness to learn A solid foundation in experimental research, data analysis, and scientific methods Interest in machine learning and data-driven approaches to materials discovery Strong interest in hands
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, computer engineering and/or computer science towards producing relevant and impactful health-monitoring mobile/wearable solutions, then please apply. The research will be highly collaborative; you should be
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level of scientific independence and will gain relevant experience to apply for independent group leader positions. In particular, you can co-supervise Master or PhD students and participate in teaching