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Computer Science, Artificial Intelligence, or related field. Proven experience in machine learning and neural network architectures. Strong programming skills in Python and familiarity with PyTorch. Experience with
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Contract length : 2 years, renewable twice, max. 6 years Rate : 100% The Department of Computational Biology (DBC ) is internationally renowned for its excellence in research pertaining to the analysis
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the “Basisprüfung” or its equivalent with good grades. MSc students and BSc students in the fifth semester or higher are preferred, although students earlier in their program will be considered if their grades
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. Profile You hold a Master's degree in a relevant field such as data science, computer science, physics, computational biology, or biomedical research. You are proficient in Python programming, with
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Emulators of Stochastic computational models"), funded by the Swiss National Science Foundation (SNSF). The project aims to significantly advance the state-of-the-art in uncertainty quantification (UQ) by
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informatics in rehabilitation, particularly in the development and implementation of digital solutions to support patient care, is advantageous, as is the ability to directly link scientific findings with
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Emulators of Stochastic computational models"), funded by the Swiss National Science Foundation (SNSF). The project aims to significantly advance the state-of-the-art in uncertainty quantification (UQ) by
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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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Theory, Queuing Theory, Age of Information), Network Calculus, Graph Theory, Convex and Non-convex Optimization, Approximation Algorithms. An excellent Master’s degree in Computer Science, Engineering
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science, chemistry, electrical engineering or a related discipline Strong background in materials informatics and data science Proficiency in Python programming Experience in machine learning for materials