22 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"EMBL" positions at Utrecht University in Netherlands
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the opportunity to develop your teaching skills (University Teaching Qualification, https://www.uu.nl/en/education/centre-for-academic-teaching-and-learning/teacher-development/utq-and-stq ). You
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Postdoc: Machine learning for wind flow prediction in coastal dunes Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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PhD position: Develop Hybrid Machine Learning for Global Soil Mapping Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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functioning in children. Childhood cognitive development shapes health and success later in life, understanding how environmental risk factors alter children's brain function, learning and behaviour
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about the Virology Section and our research directions: https://www.uu.nl/en/organisation/faculty-of-veterinary-medicine/veterinary-research/research-programmes/infection-immunity/virology More
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research, both within the field of Clinical Psychology. Your academic profile aligns with the mission and vision of the Clinical Psychology department (https://www.uu.nl/en/organisation/clinical-psychology
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connections between individuals, groups, and institutions contribute to new pathways to and forms of social cohesion. See https://socion-program.org/research/ For more information about this position
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design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and
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criteria: A PhD (obtained, or nearly completed) in Natural Language Processing or a closely related area. Solid knowledge of machine learning, especially deep learning. Experience in model development
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foundational AI and machine learning models (e.g., multimodal AI, large language/world models) with specific finetuning for ELEVATE; designing geographically context-sensitive urban design recommendations that