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Field
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representations developed in them as a foundation for this research activity. In this project, you will develop fundamental machine learning methods and apply them in an interdisciplinary research environment
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) equipped with a cryogenic stage for surface analysis - Develop computer code (e.g. Python) for the development and analysis of optical cavities Qualifications § PhD degree in Materials, Electrical
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modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in or experience in environmental exposure
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Species Spread Faculty Mentors: Dr. Tih-Fen Ting (Ecology, Environmental Science), Dr. Yanhui Guo (AI, Computer Vision), and Dr. Yun Zhao (Remote Sensing, Drone-Based Environmental Monitoring) Focus
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, particularly Python; (ii) use of parameter optimization algorithms, particularly PEST and PEST++; (iii) remote sensing applied to the water cycle; and (iv) application of machine learning techniques to spatio
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(3) years in a relevant field (e.g., Computer Science, Computational Linguistics, Data Science or related disciplines) Strong experience or demonstrated interest in AI, NLP, machine learning
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in artificial intelligence (AI) and/or machine learning. · Ability to support grant applications and ongoing research project development as well as prepare manuscripts, policy briefs, technical
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for a period of four years are expected to acquire basic pedagogical competency during their fellowship period within the duty component of 25 %. Project description and work tasks Particle accelerators
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immunology. The projects focus on learning from bats with exceptional healthy longevity to develop bat-inspired new targets and strategies to fight human diseases and ageing and have multiple national and
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). The candidate will be working on developing state-of-the-art methodology in clinical prediction modeling, including novel uncertainty assessment method (Value of Information analysis), as well as Machine Learning