PhD Candidate: Novel Methods to Understand Animal Movement
PhD Candidate: Novel Methods to Understand Animal Movement
You cannot apply for this job anymore (deadline was 27 Oct 2023).
Browse the current job offers or choose an item in the top navigation above.
Job description
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 methods to ecological data to uncover interactions between animal movement, vegetation, climate and human activities, with implications for ecosystem processes.
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.
Animal movement arises from the complex interplay between an animal's internal state and biotic and abiotic external factors such as climate, human activities and food availability. However, current methodological approaches used to gain understanding of what shapes animal movement patterns are based on correlation, ignoring potential cause-effect relationships and confounding effects between variables. Ignoring such relationships impacts our ability to make ecological inferences about the complex interactions between animals and their environment, potentially resulting in incorrect, or even missing, cause-effect relationships.
In this project you will apply modern causal discovery algorithms for time-series data and process-based 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.
Besides your research, you will have a standard teaching load of 10% to help you develop your teaching skills and further qualify for a career as an independent academic researcher.
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.
Animal movement arises from the complex interplay between an animal's internal state and biotic and abiotic external factors such as climate, human activities and food availability. However, current methodological approaches used to gain understanding of what shapes animal movement patterns are based on correlation, ignoring potential cause-effect relationships and confounding effects between variables. Ignoring such relationships impacts our ability to make ecological inferences about the complex interactions between animals and their environment, potentially resulting in incorrect, or even missing, cause-effect relationships.
In this project you will apply modern causal discovery algorithms for time-series data and process-based 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.
Besides your research, you will have a standard teaching load of 10% to help you develop your teaching skills and further qualify for a career as an independent academic researcher.
Specifications
- max. 40 hours per week
- €2770—€3539 per month
- Nijmegen View on Google Maps
:fill(white)/logos/run-en-wide.jpg)
Radboud University
Requirements
- You have a Master’s degree in (Quantitative) Ecology, Computer Science or a relevant related field.
- You have a strong interest in statistical and process-based ecological modelling.
- You have a strong interest in working on the interface between machine learning, AI and ecology.
- You enjoy working in multidisciplinary, international teams.
- You possess good communication skills in both written and spoken English.
Conditions of employment
Fixed-term contract: You will be employed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years (4 year contract).
- It concerns an employment for 1.0 FTE.
- The gross starting salary amounts to €2,770 per month based on a 38-hour working week, and will increase to €3,539 in the fourth year (salary scale P).
- You will receive 8% holiday allowance and 8.3% end-of-year bonus.
- You will be employed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years (4 year contract).
- You will be able to use our Dual Career and Family Care Services. Our Dual Career and Family Care Officer can assist you with family-related support, help your partner or spouse prepare for the local labour market, provide customized support in their search for employment and help your family settle in Nijmegen.
- Working for us means getting extra days off. In case of full-time employment, you can choose between 30 or 41 days of annual leave instead of the legally allotted 20.
Employer
This position will include working with academics from both the Department of Environmental Science and the Data Science group at the Institute for Computing and Information Sciences.
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.
Environmental Science is part of the Radboud Institute for Biological and Environmental Sciences (RIBES) at the Faculty of Science. RIBES, consisting of three research clusters (Ecology & Physiology, Microbiology, and Environmental Science), aims to understand the response of the natural environment to human impact and investigates stress and adaptation processes in severely affected ecosystems. This fundamental research is cross-linked with innovative applications in both industry and society with a focus on mitigating ecosystem degradation and climate change and finding solutions to restore the natural environment. We value a diverse workforce and strongly encourage candidates from non-traditional backgrounds and historically marginalised or underrepresented groups to apply.
The Data Science group is embedded in the Institute for Computing and Information Sciences at the Faculty of Science, and 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 1) the design and understanding of deep/causal machine learning methods, 2) modern information retrieval and big data, and 3) computational immunology, with a keen eye on applications in other scientific domains as well as industry.
The Data Science group is part of the vibrant and growing Institute for Computing and Information Sciences (iCIS), consistently ranked as one of the top Computer Science departments in the Netherlands (National Research Review Computer Science). Our group currently consists of 50 researchers, including some 25 PhDs, and offers a very open, inclusive and supportive work environment.
Radboud University
We are keen to meet critical thinkers who want to look closer at what really matters. People who, from their expertise, wish to contribute to a healthy, free world with equal opportunities for all. This ambition unites more than 24,000 students and 5,600 employees at Radboud University and requires even more talent, collaboration and lifelong learning. You have a part to play!
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.
Environmental Science is part of the Radboud Institute for Biological and Environmental Sciences (RIBES) at the Faculty of Science. RIBES, consisting of three research clusters (Ecology & Physiology, Microbiology, and Environmental Science), aims to understand the response of the natural environment to human impact and investigates stress and adaptation processes in severely affected ecosystems. This fundamental research is cross-linked with innovative applications in both industry and society with a focus on mitigating ecosystem degradation and climate change and finding solutions to restore the natural environment. We value a diverse workforce and strongly encourage candidates from non-traditional backgrounds and historically marginalised or underrepresented groups to apply.
The Data Science group is embedded in the Institute for Computing and Information Sciences at the Faculty of Science, and 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 1) the design and understanding of deep/causal machine learning methods, 2) modern information retrieval and big data, and 3) computational immunology, with a keen eye on applications in other scientific domains as well as industry.
The Data Science group is part of the vibrant and growing Institute for Computing and Information Sciences (iCIS), consistently ranked as one of the top Computer Science departments in the Netherlands (National Research Review Computer Science). Our group currently consists of 50 researchers, including some 25 PhDs, and offers a very open, inclusive and supportive work environment.
Radboud University
We are keen to meet critical thinkers who want to look closer at what really matters. People who, from their expertise, wish to contribute to a healthy, free world with equal opportunities for all. This ambition unites more than 24,000 students and 5,600 employees at Radboud University and requires even more talent, collaboration and lifelong learning. You have a part to play!
Specifications
- PhD; Research, development, innovation; Technical and laboratory
- Engineering
- max. 40 hours per week
- €2770—€3539 per month
- University graduate
- 1215086
