PhD Funded Position in Phytoplankton Trait Diversity and Ecosystem Modelling at NIOO-KNAW, Netherlands

The Netherlands Institute of Ecology (NIOO-KNAW) in Wageningen, the Netherlands, is inviting applications for a PhD position in phytoplankton trait diversity and ecosystem modelling. The doctoral researcher will investigate how diversity in phytoplankton resource-acquisition traits influences lake ecosystem structure, productivity, biomass, and functioning.

The PhD project is part of the BILMo (Biodiversity Inspired Lake Modelling) project, which is funded through the SNF-Lead Agency programme. BILMo aims to improve mechanistic lake models so they can better predict how aquatic communities respond to environmental changes such as warming and shifts in light, nitrogen, and phosphorus availability.

The successful candidate will integrate phytoplankton trait diversity into the mechanistic ecosystem model PCLake+. The research will combine ecological modelling, meta-analysis, large datasets, scenario analysis, and collaboration with researchers working on phytoplankton ecology and lake ecosystems.

This PhD position in the Netherlands is particularly suitable for candidates with an MSc degree in environmental sciences, computational ecology, quantitative biology, or a closely related discipline. Experience or a strong interest in ecosystem modelling, ecological forecasting, phytoplankton ecophysiology, or trait-based ecology will be an advantage.

The position involves collaboration between NIOO-KNAW and Wageningen University, with additional international collaboration involving researchers at EAWAG and the University of Rhode Island. The PhD candidate will work under the guidance of Dr. Dedmer van de Waal and Dr. Mandy Velthuis and is expected to complete the doctoral thesis within the official four-year appointment period.

PhD Research Project: Integrating Trait Diversity into Ecosystem Models

Aquatic ecosystems are influenced by many interacting environmental factors, including temperature, light, and nutrient availability. Phytoplankton communities respond to these conditions in different ways because individual species and populations possess different functional traits.

These differences in traits can affect how efficiently phytoplankton acquire resources, grow, compete, and contribute to ecosystem processes. However, many ecosystem models simplify biological communities and may not fully represent the consequences of inter- and intraspecific trait diversity.

The BILMo project seeks to address this limitation by incorporating biodiversity into mechanistic lake models. Its broader objective is to improve predictions of community composition and ecosystem processes under combinations of warming and changing resource availability.

The PhD position focuses specifically on understanding how phytoplankton resource-acquisition trait diversity affects ecosystem functioning. The candidate will integrate trait information into PCLake+, a mechanistic model used to simulate lake ecosystem dynamics.

The research will examine how phytoplankton diversity influences variables such as biomass, productivity, and functional diversity. It will also investigate how environmental change may modify these relationships.

Key Details of the PhD Position

Details	Information
Position	PhD Position – Integrating Trait Diversity into Ecosystem Models
Research field	Phytoplankton Ecology, Trait-Based Ecology, Ecosystem Modelling
Project	BILMo – Biodiversity Inspired Lake Modelling
Main model	PCLake+
Institutions	Netherlands Institute of Ecology and Wageningen University
Location	Wageningen, Netherlands
Supervisors	Dr. Dedmer van de Waal and Dr. Mandy Velthuis
Project coordinator	Dr. Anita Narwani, EAWAG
Contract duration	Four years
Research focus	Phytoplankton trait diversity and lake ecosystem functioning
Methods	Mechanistic modelling, meta-analysis, sensitivity analysis, scenario analysis
Study systems	Swiss lakes
Required degree	MSc in Environmental Sciences, Computational Ecology, Quantitative Biology, or related field
Programming	R, Python, Matlab or related experience
Working language	English

What Will the PhD Researcher Do?

The successful candidate will contribute to improving scientific understanding of the relationship between phytoplankton traits and lake ecosystem functioning.

A major part of the PhD will involve determining how phytoplankton interspecific and intraspecific trait diversity affects the biomass, productivity, and functional diversity of plankton communities.

The researcher will use PCLake+ to perform sensitivity analyses and explore how different representations of community diversity influence model predictions. Both traditional community modelling approaches and newer emergent community modelling approaches will be considered.

Another important component will be conducting meta-analyses to establish the parameter space associated with phytoplankton resource-acquisition traits. These analyses will help provide realistic trait information for the ecosystem model.

The candidate will subsequently conduct scenario analyses tailored to a set of Swiss lakes. These scenarios will help investigate how changes in environmental conditions may affect phytoplankton communities and the ecosystem processes they support.

Close collaboration with the other PhD candidate and postdoctoral researchers will be essential. The candidate will contribute to model parameterization and validation while exchanging ideas and research results with the broader BILMo team.

The position also includes opportunities to supervise MSc students interested in modelling phytoplankton trait diversity. In addition, the researcher will participate in annual project meetings and contribute to selected outreach activities.

As part of the PhD, the researcher will develop a thesis plan, conduct the research, analyze results, present findings at scientific meetings and conferences, and publish research articles in international scientific journals.

Understanding Phytoplankton Trait Diversity

Phytoplankton are not a single uniform group of organisms. Different taxa possess different physiological and ecological characteristics that influence how they respond to their surroundings.

Traits related to resource acquisition can determine how efficiently organisms obtain light and nutrients and how they perform when environmental conditions change.

Importantly, diversity exists both between species and within species. Two different phytoplankton species may have contrasting resource-acquisition strategies, while populations belonging to the same species may also differ in their functional characteristics.

Including this variation in ecosystem models can provide a more realistic representation of how biological communities respond to environmental change.

The BILMo project therefore moves beyond treating phytoplankton as simplified functional groups. By integrating trait diversity into mechanistic models, the researchers aim to better understand how biodiversity influences ecosystem processes.

Why Ecosystem Modelling Matters

Mechanistic ecosystem models provide researchers with tools for exploring complex interactions between organisms and their physical and chemical environments.

Lake ecosystems are influenced by temperature, light, nutrient availability, biological competition, and many other factors. Understanding these interactions experimentally can be challenging, particularly when researchers want to examine multiple environmental scenarios simultaneously.

Models such as PCLake+ can help researchers simulate ecosystem responses under different conditions and investigate mechanisms that may be difficult to isolate through observations alone.

The PhD project combines these modelling approaches with empirical trait information and meta-analysis. This allows the candidate to investigate not only what happens when environmental conditions change, but also why community and ecosystem responses occur.

BILMo: Biodiversity Inspired Lake Modelling Project

The doctoral position is part of the BILMo project, a collaborative research initiative focused on improving lake ecosystem models by incorporating biodiversity and community composition.

The wider project includes five interlinked objectives addressing community responses to warming and changes in resource availability, including light, nitrogen, and phosphorus.

BILMo brings together field observations, laboratory experiments, and mechanistic modelling. The project team includes two PhD candidates and three postdoctoral researchers, together with researchers from several participating institutions.

The wider team includes Dr. Anita Narwani from EAWAG, Dr. Keisuke Inomura from the University of Rhode Island, Prof. Damien Bouffard from EAWAG, Prof. Dedmer van de Waal from NIOO-KNAW, and Dr. Mandy Velthuis from Wageningen University.

This international structure provides the PhD researcher with opportunities to interact with scientists working across ecological modelling, phytoplankton biology, lake ecology, and environmental change.

Eligibility and Candidate Profile

Applicants should hold an MSc degree in environmental sciences, computational ecology, quantitative biology, or a closely related discipline.

The ideal candidate will have a strong interest in plankton trait-based ecology and ecosystem modelling. A genuine enthusiasm for understanding ecological processes through quantitative approaches will be important for the project.

Previous experience with ecosystem modelling, ecological forecasting, phytoplankton ecophysiology, or trait-based ecology is considered an advantage. However, candidates are not expected to master every technique from the beginning.

The position is designed for someone who is willing to learn and develop new skills throughout the doctoral programme. Experience with one or more of the listed research areas can provide a useful starting point.

Candidates should also be comfortable working with large datasets and modelling pipelines. An interest in developing advanced programming skills using software such as R, Python, or Matlab is important.

The successful applicant should be well-organized and able to work independently while also contributing effectively to a collaborative research team.

Professional working proficiency in English, including scientific writing, is required.

Desirable Research Experience

Previous experience in one or more of the following areas would be particularly valuable:

• Ecosystem modelling
• Ecological forecasting
• Phytoplankton ecophysiology
• Trait-based ecology
• Quantitative ecology
• Large ecological datasets
• Scientific programming
• Model parameterization and validation

However, the project does not require candidates to have expertise in every listed area.

The vacancy specifically emphasizes the candidate’s willingness to develop advanced skills. Therefore, applicants with a strong quantitative background and clear motivation to learn ecological modelling may also find the position suitable.

Programming and Data Analysis Skills

Computational work will be an important part of this PhD position.

The researcher will work with large datasets, modelling pipelines, trait parameters, and ecosystem scenarios. Programming can therefore play an important role in data processing, model implementation, sensitivity analysis, visualization, and interpretation.

Experience with R, Python, Matlab, or similar programming environments is beneficial. Candidates who already have experience with scientific computing or quantitative ecological analysis may be particularly well prepared for the project.

At the same time, the position provides an opportunity to strengthen programming abilities during the PhD. Candidates do not need to be experts in every computational method from day one.

Research Skills You Can Develop

This doctoral project offers an interdisciplinary combination of ecology, biodiversity research, mathematical modelling, data analysis, and environmental forecasting.

The successful candidate can develop advanced skills in mechanistic ecosystem modelling using PCLake+, including sensitivity analysis, parameterization, validation, and scenario analysis.

The project will also provide experience in conducting meta-analyses and translating information from the scientific literature into model parameters.

Working with phytoplankton trait data can strengthen expertise in trait-based ecology and functional diversity, while the Swiss lake scenarios can provide experience in applying ecological models to realistic environmental systems.

Additional transferable skills will come from scientific writing, conference presentations, publication preparation, student supervision, project collaboration, and scientific outreach.

Collaboration with Wageningen University and International Partners

The PhD researcher will work both at NIOO-KNAW and Wageningen University, creating an opportunity to interact with researchers across institutional and disciplinary boundaries.

The candidate will work closely with the other BILMo PhD researcher and postdoctoral researchers on model parameterization and validation.

The international project structure also connects the research to scientists at EAWAG and the University of Rhode Island. Regular exchange of ideas and results will be an important part of the BILMo research environment.

For a doctoral researcher interested in international collaboration and interdisciplinary ecological modelling, this structure offers valuable exposure to different scientific perspectives.

Workplace: NIOO-KNAW and Wageningen

The position is based at the Netherlands Institute of Ecology (NIOO-KNAW) and Wageningen University in the Netherlands.

NIOO-KNAW is a national research institute of the Royal Netherlands Academy of Arts and Sciences (KNAW). Its research focuses on major ecological challenges including biodiversity, climate change, and sustainable use of land and water.

Wageningen provides a strong academic environment for research related to environmental sciences, ecology, natural resources, and sustainability. Working across NIOO-KNAW and Wageningen University can therefore provide the PhD researcher with access to a broad scientific network.

Training and Professional Development

Further training and courses will be available through NIOO-KNAW, Wageningen University, and the Graduate School for Production Ecology & Resource Conservation (PE&RC).

These opportunities can help the researcher build both specialized knowledge and transferable skills throughout the four-year PhD trajectory.

Training may complement the candidate’s work in ecosystem modelling, ecological data analysis, scientific communication, and research management.

Participation in scientific meetings and collaboration with researchers from different institutions will also contribute to the candidate’s professional development.

Salary and Employment Conditions

The vacancy offers a four-year PhD appointment, with the aim of completing the doctoral thesis within the official appointment duration.

Depending on education and experience, the salary ranges from €3,204 to €4,051 gross per month for a full-time appointment. The position follows scale P under the cao Nederlandse Universiteiten/KNAW.

In addition to the basic salary, the employment package includes an 8% vacation allowance and an 8.3% year-end bonus.

Other benefits include travel allowance, internet allowance, home-working allowance, and pension accrual through ABP.

KNAW also provides secondary employment benefits designed to accommodate different stages of employees’ lives and career ambitions.

For full-time employees, working an additional two hours per week can allow annual leave to increase from 29 to 41 days per year.

A Certificate of Good Conduct may also form part of the employment procedure.

About NIOO-KNAW

The Netherlands Institute of Ecology (NIOO) is a national research institute of the Royal Netherlands Academy of Arts and Sciences (KNAW).

NIOO conducts ecological research focused on major environmental challenges, including biodiversity, climate change, and sustainable use of land and water.

The institute also supports ecological research in the Netherlands and works to communicate ecological knowledge to wider society.

For a PhD investigating phytoplankton biodiversity, ecosystem functioning, and environmental change, NIOO-KNAW provides a highly relevant ecological research setting.

Diversity and Inclusion

KNAW places importance on creating a working environment where everyone feels welcome and appreciated.

The organization emphasizes individual quality, professional development, and an inclusive culture that embraces differences.

Candidates are encouraged to contribute to this environment through their backgrounds and experiences. In cases of equal suitability, preference may be given to a candidate who enhances diversity within the Academy.

How to Apply for the PhD Position

Applications must be submitted in English electronically through APPLY NOW Applicants should prepare a cover letter explaining their motivation and relevant experience, together with a CV and details for 1–3 professional referees. Referee information should include the person’s name, address, telephone number, and email address.

Candidates should use the application to demonstrate their interest in phytoplankton trait-based ecology, ecosystem modelling, biodiversity, and environmental change. Relevant experience with ecological modelling, quantitative analysis, programming, large datasets, or phytoplankton research should also be clearly presented. For further information about the project, applicants can contact Dedmer van de Waal at d.vandewaal@nioo.knaw.nl or Mandy Velthuis at mandy.velthuis@wur.nl.

Frequently Asked Questions

1. What is the topic of this PhD position?

The PhD investigates how phytoplankton trait diversity affects lake ecosystem structure and functioning using mechanistic ecosystem modelling.

2. What model will be used?

The research will use PCLake+, a mechanistic lake ecosystem model, to investigate the consequences of phytoplankton trait diversity.

3. What is the BILMo project?

BILMo stands for Biodiversity Inspired Lake Modelling. The project aims to improve mechanistic lake models by incorporating biodiversity and community composition into ecosystem predictions.

4. Where is the PhD position located?

The researcher will work at NIOO-KNAW and Wageningen University in the Netherlands.

5. How long is the PhD appointment?

The official appointment duration is four years, with the aim of completing the PhD thesis within this period.

6. What degree is required?

Applicants should have an MSc degree in environmental sciences, computational ecology, quantitative biology, or a closely related discipline.

7. Is ecosystem modelling experience required?

Experience with ecosystem modelling, ecological forecasting, phytoplankton ecophysiology, or trait-based ecology is an advantage. Candidates are not expected to master all techniques from the beginning.

8. Which programming languages are relevant?

The vacancy specifically mentions R, Python, and Matlab. Candidates should be eager to develop advanced programming skills.

9. Will the PhD involve large datasets?

Yes. The candidate will work with large datasets, modelling pipelines, trait parameters, and ecosystem scenarios.

10. What research methods will be used?

The PhD will involve PCLake+ sensitivity analysis, meta-analysis, model parameterization and validation, and scenario analysis.

11. What lakes will be studied?

The scenario analyses will be tailored to a set of Swiss lakes.

12. Will the PhD researcher work with other researchers?

Yes. The candidate will collaborate closely with another PhD researcher, postdocs, and researchers from participating institutions in the BILMo project.

13. Will the PhD researcher supervise students?

Yes. The position includes opportunities to supervise MSc students working on internships involving phytoplankton trait-diversity modelling.

14. What is the salary?

The salary ranges from €3,204 to €4,051 gross per month for a full-time appointment, depending on education and experience.

15. What language is required?

Applicants need professional working proficiency in English, including the ability to write scientifically.