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PhD Candidate in Exposomics, Machine Learning and Artificial Intelligence Faculty: Faculty of Veterinary Medicine Department: Department Population Health Sciences Hours per week: 36 to 40
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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processing, (mathematical foundations of) machine learning, and dynamical systems. Your job In this position, you will divide your time roughly equally between research and teaching (approximately 50% each
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collaborative, diverse team. This is a unique opportunity to contribute to the foundations for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic
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, including abstract geospatial workflows; design AI- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute
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transformed not only how we read, but also how we learn to read, bringing both new challenges and exciting opportunities. To fully understand these developments, this project explores the processes underlying
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. Work Style: Independent, well-organized, and eager to learn new skills. Teamwork: Strong interpersonal skills, a collaborative mindset, and a flexible approach to work. Language Proficiency: Fluency in
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to inspire students through research-based and active learning strategies. In addition, you: conduct innovative and internationally visible research in your area of expertise with scientific and
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interdisciplinary research team; learn to independently collect, analyse and interpret your data, share your data at national and international conferences, and prepare manuscripts for publication; contribute
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, and PhD levels while leading curriculum innovation to enhance teaching and learning; supervising PhD candidates, postdoctoral researchers, junior faculty members and graduate students; providing