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PhD position in Causal Time-series Analysis for Ecology (1.0 FTE, 4yrs.)
Institution : Radboud University Nijmegen, Netherlands
Keywords : causal discovery, ecological modelling, machine learning,
time-series analysis
Application deadline : 27 October 2023
Website :
https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ru.nl%2Fen%2Fworking-at%2Fjob-opportunities%2Fphd-candidate-novel-methods-to-understand-animal-movement&data=05%7C01%7Cuai%40engr.orst.edu%7Cd0ad2b04ca7f4b96dd3708dbc8c97527%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638324539810739296%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=KM8PPCoYGTK7OIsS9KQNFUI1a4t4VYXtRjIz%2FYs%2BonE%3D&reserved=0
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Summary
Are you interested in applying new machine learning methods and process-based
models to understand how humans are impacting biodiversity? Come work with us
to develop and apply new causal methods to ecological data to uncover
interactions between animal movement, vegetation, climate and human activities,
with implications for ecosystem processes.
Description
We are currently experiencing a biodiversity crisis and one of the main drivers
is human activities. As human activities expand, animal behaviour is being
altered. One behaviour that is drastically affected is animal movement. Animal
movement is an important process determining the fate of individuals and
shaping the structure and dynamics of populations and ecosystem processes.
Therefore, changes in movement will have wide-ranging ecological consequences.
In this project you will apply modern causal discovery algorithms for
time-series data and process-based ecological modelling to examine the
mechanisms of animal movement and explore ecosystem consequences of altered
animal behaviour due to human pressures such as agricultural land conversion.
This will involve the analysis of empirical animal movement data, environmental
data, human pressure data, and species traits to explore interactions between
animals and their surroundings.
We are:
The project is a collaboration between the Environmental Science cluster at the
Radboud Institute for Biological and Environmental Sciences (RIBES), and the
Data Science group of the Institute for Computing and Information Sciences
(iCIS), both part of the Faculty of Science at the Radboud University. You will
be working in both groups, at the interface of ecology and machine learning.
The mission of the Environmental Science research cluster of Radboud University
is to provide high-quality scientific knowledge that can be used to help people
move towards a more sustainable society. To achieve this, we aim to understand,
project and address the impact of anthropogenic pressures on ecosystems and
humans, from the landscape to the global scale.
The Data Science group within iCIS aims to develop theory and methods for
scalable machine learning and information retrieval to address challenging
problems in science and society. The group's main research foci are: the design
and understanding of deep / causal machine learning methods, modern information
retrieval / big data, and computational immunology, each with a keen eye on
applications in other scientific domains as well as industry.
Both groups strongly promote an open, inclusive and supportive work environment.
What we expect from you:
You have an MSc degree in natural science, ecology, computer science, or a
related discipline. You are open-minded, with a strong interest in
multidisciplinary research, especially on the interface between machine
learning and ecology. 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.
For more information about this vacancy and details on how to apply, see the
website (above), or contact:
* Dr Marlee Tucker, e-mail: marlee.tuc...@ru.nl
<mailto:a.schip...@science.ru.nl> (RIBES)
* Dr Tom Claassen, tel: +31 24 3652019, e-mail: tom.claas...@ru.nl
<mailto:t...@cs.ru.nl> (iCIS)
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