15 machine-learning-modeling-"Linnaeus-University" PhD positions at Utrecht University
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geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data
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University, you will build models and methods to parse natural language questions into geo-analytical workflows, combining NLP and semantic representations to improve how complex spatial questions can be
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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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dune development and increase the applicability of coastal dune models. Your job In this project, you will investigate dune erosion and growth by performing morphological analysis on existing coastal
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techniques, high-throughput screening and precise genome editing. Your job You will join the Vlaming lab , within the Genome Biology & Epigenetics division. The aim of your PhD project is to learn
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qualitative research, systematic network analysis, and the design of educational and communication models that empower professionals to implement new approaches in their fields. Your job The Netherlands is
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.); computational skills to analyse social media data (e.g., with Natural Language Processing, LLMs), and/or a strong motivation to learn these skills; excellent oral and written command of Dutch and English
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for delta adaptation and development under uncertain changing conditions? How can we sequence measures that are made in different regions, e.g. using modelling tools? What is the timing of decisions and what
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post-zygotic aneuploidy arises in mammalian embryos and its consequences during early pregnancy. You will primarily work with equine embryos, which represent a valuable model for studying post-zygotic
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of methane dynamics in rapidly changing ecosystems and contribute to improving predictive models of future methane emissions. Field sampling will focus on regions where methane cycling is still poorly