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The Department of Physics, Chemistry and Biology at Linköping Univertsity invites applications for a Doctoral Candidate position within the Marie Skłodowska-Curie Actions Doctoral Network ReDiLEEP (Using science-driven Response Diversity knowledge to Leverage Efficient Ecosystem Preservationand restoration). ReDiLEEP brings together leading universities, research institutes, and non-academic partners across Europe and beyond to advance response diversity research and translate new knowledge into ecosystem management, restoration, and biodiversity policy. The Doctoral Candidate will contribute to this effort by investigating the relationship between the structural organization of ecological networks and response diversity.
Response diversity describes the variation in how individuals, species, or functional groups respond to environmental change and the concept is increasingly recognised as a key driver of ecosystem resilience. Understanding and promoting response diversity is therefore essential for conserving biodiversity and supporting resilient ecosystems in a changing world.
Acoustic monitoring provides a powerful, non-invasive way to track species and their interactions over time. DC2 will use a unique 10-year acoustic dataset from Okinawa, Japan, to investigate how species interactions and network structure shape response diversity under environmental change. The project will develop and apply AI and machine-learning methods to infer interaction networks from acoustic co-occurrence and activity patterns, and analyse their structural properties to link topology to response diversity. These will be used to study how interactions and response diversity change along an urbanisation gradient and testing how network architecture buffers or amplifies the effects of disturbance.
As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time.
Secondments and Network Training
The position includes 9 months of planned secondments:
The candidate must have:
Experience with bioacoustics, machine learning, ecological network analysis, signal processing, or analysis of large ecological datasets is an advantage.
A letter of intent should be attached to the application to support the above mentioned experiences and interests.
MSCA Eligibility Criteria
Applicants must meet the Marie Skłodowska-Curie Actions Doctoral Network eligibility requirements at the recruitment date:
What We Offer
When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping University
The employment has a duration of normally four years’ full-time equivalent. Extension of employment up to five years is based on the degree of teaching and institutional assignment. Further extensions may be granted in exceptional circumstances. You will initially be employed for one year, after which your employment will be renewed for a maximum of two years at a time, depending on your progress through the study plan.
The position is funded by the Marie Skłodowska-Curie Actions Doctoral Networks framework and will follow the applicable MSCA Doctoral Network conditions and Linköping University employment regulations.
More information about employment benefits at Linköping University is available here.
Information about union representatives, see Help for applicants.
Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than 5 October, 2026.
Applications and documents received after the date above will not be considered.
We welcome applicants with different backgrounds, experiences and perspectives - diversity enriches our work and helps us grow. Preserving everybody's equal value, rights and opportunities is a natural part of who we are. Read more about our work with: Equal opportunities.
We look forward to receiving your application!
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