1 dag geleden - Academic Medical Center (AMC) - Amsterdam
You will initiate emerging, cutting-edge genome modification technologies across the AMC/VUmc alliance, and advance our expertise in functional cancer genomics…
Predicting how species and ecosystems will respond to global environmental change is a central goal in ecology. As controlled experiments cannot fully address this goal, there is a clear need for innovative statistical and machine ...
Predicting how species and ecosystems will respond to global environmental change is a central goal in ecology. As controlled experiments cannot fully address this goal, there is a clear need for innovative statistical and machine learning methods to analyse ecological field data.
In this PhD project you will be developing and testing novel machine learning algorithms that can be applied to reveal causal relationships from observational ecological data. Ecological monitoring data are typically characterised by multiple spatial and temporal dependencies. For example, due to auto-ecological processes such as reproduction and dispersal, species’ distribution patterns are often more clustered than would be expected based on abiotic gradients. A main challenge in this project will be to develop machine learning algorithms able to deal with such dependencies. After testing, you will apply the algorithms to large-scale ecological monitoring data in order to reveal causal relationships between species’ occurrence and underlying drivers.
The project is a collaboration between the Environmental Science group of the Institute for Water and Wetland Research (IWWR) and the Data Science group of the Institute for Computing and Information Sciences. You will be working in both groups, at the interface of ecology and machine learning.
You have an MSc degree (for the PhD position) or a PhD (for the postdoc position) in natural science, computer science, mathematics, or a related discipline. You are open-minded, with a strong interest in multidisciplinary research and a solid background in mathematics, and you are highly motivated to perform scientific research. As you will be working in two different research groups, you need to be flexible, communicative and able to work in a multidisciplinary team.
Strategically located in Europe, Radboud University is one of the leading academic communities in the Netherlands. A place with a personal touch, where top-flight education and research take place on a beautiful green campus in modern buildings with state-of-art facilities.
Faculty of Science
The main focus of the Environmental Science group of IWWR is on quantifying, understanding and predicting human impacts on the environment. To that end, we employ a variety of research methods, including process-based modelling, meta-analyses, field studies and lab work. In our research we cover multiple stressors, species and spatial scales, searching for overarching principles that can ultimately be applied to better underpin environmental management and biodiversity conservation. The Data Science group’s research concerns the design and understanding of (probabilistic) machine learning methods, with a keen eye on applications in other scientific domains as well as industry. The Data Science section is part of the vibrant and growing Institute for Computing and Information Sciences (iCIS). iCIS is consistently ranked as the top Computer Science department in the Netherlands (National Research Review of Computer Science 2002-2008 and 2009-2014).
No commercial propositions please.
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