项目介绍

2025年4月11日

Job description

Species interact in complex biological networks e.g. foodwebs, forming the foundation of natural ecosystems. Yet, we lack the tools to predict how these networks change in time and space. This is especially critical given the increased pressure from human activities that push species to extinction and potentially disrupts ecosystem functionality. Our interdisciplinary lab will develop novel Graph Representation Learning models to understand and predict interactions in dynamic ecological networks.Our lab is looking for candidates for the following four stipends:

Stipend 1:  Environmental and biotic drivers of ecological network structure
A PhD stipend is available in network biology and biogeography. We seek a PhD candidate to investigate the role of environmental/macroecological factors and species traits in driving the structure of ecological networks. The candidate will use existing interaction databases and acquire networks from the literature, e.g. on seed-dispersal and pollination networks as well as food-webs. The candidate will use novel representation learning methods to study graph-structured ecological data. We are looking for a highly collaborative candidate to work closely with other members of the AI:EcoNet Lab.

Requirements:

• Master’s degree in biology with a strong interest in data science
• Strong background in ecology and/or biogeography
• Experience with ecological networks, geospatial analysis and machine learning is advantageous
• Interest in data mining and database management
• Programming skills e.g. in R or Python
• Good analytical and communication skills. Proficiency in written and spoken English is mandatory

Stipend 2:  Species interaction networks in a changing world
We seek a PhD candidate to develop methods for merging network ecology with species distribution modelling to investigate the impacts of environmental change on species interactions networks. Specifically, the project will explore how ecological networks change over time as species range shifts result in loss or gain of interactions. The candidate will also implement advanced graph-learning models to infer interactions within various ecological networks. We are looking for a highly collaborative candidate to work closely with other members of the AI:EcoNet Lab.

Requirements:

• Master’s degree in biology with a strong interest in data science
• Strong background in ecology and/or biogeography
• Experience with species distribution models, ecological network analysis, and/or geospatial analysis is advantageous
• Programming skills e.g. in R or Python and machine learning experience
• Good analytical and communication skills. Proficiency in written and spoken English is mandatory

Stipend 3:  Learning the Structure and Dynamics of Complex Networks
We seek a PhD candidate to develop novel representation learning methods on graphs for modeling temporal networks, with applications to ecological systems and beyond. The project will focus on advancing scalable approaches to capture how networks evolve over time. We are looking for a highly collaborative candidate to work closely with other members of the AI:EcoNet Lab.

Requirements:

• Master’s degree in computer science, data science, mathematics, statistics, physics, software or relevant fields
• Strong background in machine learning, preferably experience in graph representation learning or time series analysis
• Programming skills (e.g., Python) and experience with deep learning frameworks (e.g. PyTorch)
• Interest in applications to ecological or biological networks
• Good analytical and communication skills. Proficiency in written and spoken English is mandatory

Stipend 4:  Joint Modeling of Graph-Structured Data
This PhD project will develop novel methods for joint learning of network structure and node attributes in complex systems. The research will focus on learning unified representations that capture both interaction patterns and node features, with applications to ecological networks (e.g. species interaction networks) and other attributed network domains. We are looking for a highly collaborative candidate to work closely with other members of the AI:EcoNet Lab.

Requirements:

• Master’s degree in computer science, data science, mathematics, statistics, physics, software or relevant fields
• Background in machine learning, preferably experience in graph representation learning or multimodal learning
• Programming skills (e.g., Python) and experience with deep learning frameworks (e.g. PyTorch)
• Interest in applications to ecological or biological networks
• Good analytical and communication skills. Proficiency in written and spoken English is mandatory

Further information
You may obtain further information concerning the scientific aspects of the stipends for project 1 and 2 from Associate Professor Michael Ørsted, Department of Chemistry and Bioscience, (email: moer@bio.aau.dk) and for project 3 and 4 from Assistant Professor Abdulkadir Celikkanat, Department of Computer Science (email: abce@cs.aau.dk)

Please clearly indicate at the top of your application which of the stipend(s) you are applying for.
The application must include a brief description (maximum 1 page) of your motivation for the stipend(s).We expect to conduct interviews for all four stipends on May 19- 30.

The application must contain the following:

• Letter of motivation that includes:
  • your motivation for pursuing a PhD
  • what you hope to learn during the PhD programme
  • your experience and interest in conducting research
  • your experience and interest in studying AI and work
• Project description(which is required for technical reasons)
  • In this case, where you apply for a specific project, you may upload a copy of the project description above
• Curriculum vitae
• Diplomas confirming academic degrees (master’s degree), including academic transcripts
• Contact information of references

PhD stipends are allocated to individuals who hold a Master’s degree. PhD stipends are normally for a period of 3 years. It is a prerequisite for allocation of the stipend that the candidate will be enrolled as a PhD student at the relevant Doctoral School in accordance with the regulations of Ministerial Order No. 1039 of August 27, 2013 on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions. According to the Ministerial Order, the progress of the PhD student shall be assessed at regular points in time.

For further information about stipends and salary as well as practical issues concerning the application procedure contact Ms. Vigdis Skomager-Bohnfeldt, AAU PhD, email: vkns@adm.aau.dkFor more information on the Doctoral School of Engineering and Science: https://www.phd.aau.dk/eng (stipend 1 and 2)For more information on The Technical Doctoral School of IT and Design: https://www.phd.aau.dk/tech (stipend 3 and 4)

The application is only to be submitted online by using the “Apply online” button below.
Assessment
The assessment of candidates for the position will be carried out by qualified experts.Shortlisting will be applied. This means that after the application deadline, the head of the department, with the assistance of the hiring committee, will select the applicants to be assessed. All applicants will be informed whether they have been shortlisted for assessment or not.When the hiring process is completed, a final rejection will be sent to the applicants who are not considered for the position.AAU wishes to reflect the diversity of society and welcomes applications from all qualified candidates regardless of personal background or belief.The hiring process at Aalborg University may include a risk assessment as a tool to identify potential risks associated with new hires, ensuring the safety, compliance, and integrity of the workplace.

Wages and employment

Appointment and salary as a PhD fellow are according to the Ministry of Finance Circular of 15 December 2021 on the Collective Agreement for Academics in Denmark, Appendix 5, regarding PhD fellows, and with the current Circular of 11 December 2019 on the employment structure at Danish universities.

Ref number

50-25043

Deadline

28.04.2025