<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div class="">===========================================================</div><div class="">PhD position in Machine Learning for Ecology (1.0 FTE, 4yrs.)</div><div class="">Institution : Radboud University Nijmegen, Netherlands</div><div class="">Keywords : causal discovery, ecological modelling, machine learning, environmental change</div><div class="">Application deadline : 15 March 2017</div><div class="">Website : <a href="http://www.ru.nl/werken/details/details_vacature_0/?recid=595580" class="">http://www.ru.nl/werken/details/details_vacature_0/?recid=595580</a></div><div class="">===========================================================</div><div class=""><br class=""></div><div class="">Summary</div><div class="">The project aims to bridge the gap between state-of-the-art causal discovery algorithms and their application to observational ecological data, in order to help predicts factors that drive biodiversity under environmental change. To that end, causal discovery algorithms will be developed that are capable of handling the spatiotemporal dependencies that are common in field monitoring data. After testing, the algorithms will be applied to reveal cause-effect relationships from various ecological monitoring datasets.</div><div class="">We are looking for talented, highly motivated candidates: either students from computer science/mathematics with an interest in real-world applications, or students from biology/environmental sciences with a background in modelling and statistical analysis.</div><div class=""><br class=""></div><div class="">Description</div><div class="">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. </div><div class=""><br class=""></div><div class="">The project is a collaboration between the Data Science group of the Institute for Computing and Information Sciences and the Environmental Science group of the Institute for Water and Wetland Research (IWWR). You will be working in both groups, at the interface of ecology and machine learning.</div><div class="">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. </div><div class="">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).</div><div class=""><br class=""></div><div class="">What we expect from you:</div><div class="">You have an MSc degree in natural science, computer science, mathematics, or a related discipline. You are open-minded, with a strong interest in multidisciplinary research, and you are highly motivated to perform scientific research and obtain a PhD degree. As you will be working in two different research groups, you need to be flexible, communicative and able to work in a multidisciplinary team.</div><div class=""><br class=""></div><div class="">For more information about this vacancy and details on how to aply, see the website or contact: </div><div class="">* Dr. Aafke Schipper, tel: +31 655461524, e-mail: <a href="mailto:a.schipper@science.ru.nl" class="">a.schipper@science.ru.nl</a> (IWWR)</div><div class="">* Prof. Tom Heskes, tel: +31 24 3652696, e-mail: <a href="mailto:t.heskes@science.ru.nl" class="">t.heskes@science.ru.nl</a> (iCIS)</div><div class="">* Dr. Tom Claasen, tel: +31 24 3652019, e-mail: <a href="mailto:tomc@cs.ru.nl" class="">tomc@cs.ru.nl</a> (iCIS)</div><div class=""><br class=""></div><div class="">===========================================================</div><div class=""><br class=""></div></body></html>