PhD Topics

Individual Research Projects of the Doctoral Candidates (DCs)

WildBotics advances ecological science and technologies for nature conservation using a combination of training-by-research and with a multidisciplinary training activities and developing new robotics, AI and vision solutions within 12 collaborative DCs/PhDs, equally divided in the 3 clusters / themes of the project.

The PhDs are fully funded by the project for 36 months and should start before September 2026.

Theme 1 - Robot system design for nature conservation

DC 1: Aerial and legged robotics collaboration for flexible sampling 

Aim: This PhD project focuses on the combination of aerial and legged robotics for long-duration sampling of a local area at long range. The aerial robot deploys a smaller, slow-moving legged robot at a selected location, after which the legged robot can remain for a longer duration and perform multiple sampling operations. The aerial robot can collect data from the legged robot incrementally and can retrieve the legged robot once the local mission is completed. The core of the research is in the prototyping of such slow-moving legged robots appropriate for transportation by aerial robots and the control required for automatic deployment and retrieval of the legged robot.

Host institution: University of Southern Denmark (SDU), Denmark

PhD enrolment: University of Southern Denmark (SDU), Denmark

Principal / Academic supervisor: R. Ladig (SDU) / U. Lundquist (SDU)

DC 2: Multi-modal drones for remote sampling in challenging environments 

Aim: This PhD project will focus on the development of drones that are able to land on uneven and compliant surfaces in order to collect eDNA samples. Examples of this type of surface might include rocky outcrops, pillars, riverbeds and steep hillsides. A range of different landing mechanisms, sensors and AI based control methods will be prototyped and developed to support BVLOS (Beyond Visual Line of Sight) operations. By combining rapid onboard remote assessment with novel vehicle and undercarriage designs, landing sites that are currently inaccessible will become available for long duration sampling and monitoring.

Host institution: University of Bristol (UB), UK

PhD enrolment: University of Bristol (UB), UK

Principal / Academic supervisor: T. Richardson (UB)

DC 3: Specialised eDNA device for multiple sampling and sample preservation

Aim: The PhD project aims to advance the capabilities of mobile and aerial robots for large-scale eDNA sampling across diverse environments such as water, land, and air. The research has two main objectives: (1) To design, build, and validate mechanisms that enable the collection of multiple eDNA samples within a single autonomous mission; (2) To design, build, and validate a base station capable of receiving, preserving, and storing eDNA samples until they are retrieved. These innovations will allow mobile and aerial robots to gather extensive eDNA samples over large areas (DC12) and store them for longer durations, supporting later offline analysis.

Host institution: DMR, Denmark

PhD enrolment: University of Southern Denmark (SDU), Denmark

Principal / Academic supervisor: S. Bastholm (DMR) / U. Lundquist (SDU)

DC 4: Aerial perching and sampling via active touch: embodying safe and robust grasping of natural targets

Aim: This PhD research focuses on extending drone mission durations through agile perching, inspired by the way birds rest on tree branches. While perched, drones can collect samples and bioacoustic data in an energy-efficient and quiet manner. Autonomous perching in natural environments is hindered by uncertainties in branch shape, orientation and stability. To address these challenges, lightweight touch sensors will be integrated into the drone’s gripper, allowing it to quickly correct its approach and robustly perch on branches. The incorporation of tactile feedback in the control loop will also be used to enable sampling of delicate elements, by helping modulate grip strength and thus minimising the risk of damage to natural objects. 

Host institution: Delft University of Technology (TUD), Netherlands

PhD enrolment: Delft University of Technology (TUD), Netherlands

Principal / Academic supervisor: S. Hamaza (TUD) / M. Snellen (TUD)

Theme 2 - Autonomy, perception and AI for complex natural environments

DC 5: Multi-modal data fusion for advanced perception and navigation

Aim: The project tackles the fusion of data coming from multiple sensors (imaging, RGBD, LiDAR, radar, GNSS, IMU) in order to improve performances of SLAM methods, navigation & mapping capabilities and fully exploit data complementarity. Different sensors will be considered and tested, considering the payload and endurance capabilities of the chosen ground or aerial platforms. Sensor/data fusion will be performed at raw data level and algorithms will integrate and combine data to exploit intrinsic benefits. AI-based solutions (reinforcement learning, scene semantic segmentation, monocular depth estimation, etc.) will be also investigated together with on-board real-time performances. The aim is to go beyond state-of-the-art in SLAM and support platforms to autonomously navigate in complex environments (DC1-2-4), perform sampling operations (DC3-7) or scene understanding (DC8).

Host institution: Fondazione Bruno Kessler (FBK), Italy

PhD enrolment: University of Salamanca (USAL), Spain

Principal / Academic supervisor: F. Remondino (FBK) / S. Lagüela (USAL)

DC 6: AI-based soundscape analysis

Aim: The research explores AI-driven analysis of soundscapes to detect species and bioevents, focusing on ultrasonic recordings critical for understanding biodiversity and ecosystem health. By automating the analysis of extensive audio data, AI enables faster, efficient tracking of animal populations and ecosystem changes. This approach offers essential tools for conservation, aiding in the preservation of species and habitats, particularly those impacted by human activity or climate change. These sounds, collected by microphones and specialised devices, are crucial for identifying various animal species and understanding ecosystem dynamics. Analytics will support the study of natural soundscape losses, a reflection of declining biodiversity and disrupted ecosystems. Analytics will be based on data filtering, feature extraction and segmentation with CNN/RNN/Transformers models. AI models will be deployed for offline (post-flight) or almost real-time data processing, exploiting on-board GPU capabilities, for field work activities (DC11).

Host institution: Fondazione Bruno Kessler (FBK), Italy

PhD enrolment: University of Salamanca (USAL), Spain

Principal / Academic supervisor: A. Brutti (FBK) / D. Gonzalez-Aguilera (USAL)

DC 7: Below-canopy autonomous navigation for sampling purposes

Aim: This project addresses the development of advanced planning and navigation techniques for autonomous drone or UAV operation in challenging environments, specifically below-canopy areas where GPS signals are often unavailable. By utilising a combination of advanced perception systems and onboard processing capabilities, the UAV will employ a semantic SLAM (Simultaneous Localization and Mapping) approach to interpret its surroundings and accurately navigate to designated sampling locations. Reinforcement learning will be explored to enhance planning and scheduling tasks, enabling the UAV to efficiently adjust its route and sampling sequence. To counter the lack of GNSS data, the project will focus on alternative localisation methods, such as visual odometry or LiDAR-based positioning, ensuring robust and reliable navigation in dense environments. These innovations aim to support critical sampling tasks in forestry, agriculture and environmental monitoring (DC9-12).

Host institution: University of Salamanca (USAL), Spain

PhD enrolment: University of Salamanca (USAL), Spain

Principal / Academic supervisor: D. Gonzalez-Aguilera (USAL)

DC 8: Development of swarm robotics systems for environmental monitoring and management

Aim: This PhD research aims to advance swarm (aerial, aquatic and terrestrial) robotics for real-time environmental monitoring by developing a decentralised system of minimalist robots that autonomously gather and analyse environmental data. Going beyond traditional swarm robotics, this project integrates decentralised technologies and advanced consensus mechanisms for secure, distributed decision-making within mobile ad hoc networks (MANETs). Using leaderless consensus algorithms, these swarms will maintain robust performance even under high-latency, low-bandwidth conditions, facilitating multiple operations (e.g. sampling, DC9-10-11-12) at the same time. Additionally, digital twin technologies will enhance situational awareness, enabling seamless management of sensor data streams. This research promises scalable, resilient robotic swarms capable of autonomously performing complex tasks in dynamic, resource-limited environments, with applications ranging from biodiversity conservation to emergency response.

Host institution: Ingeniarius (ING), Portugal

PhD enrolment: University of Salamanca (USAL), Spain

Principal / Academic supervisor: M. Couceiro (ING) / D. Gonzalez-Aguilera (USAL)

Theme 3 - Analysis of large,  sample-based datasets for wildlife ecology & biodiversity conservation

DC 9: Autonomous faecal sampling to understand disease transmission in savanna ungulate communities

Aim: Faeces offer the opportunity to non-invasively gather data on the health and ecology of wild animals. However, collecting faecal samples is challenging: researchers must observe the defecation event to know which animal produced the sample, monitor the sample to ensure it is not contaminated by other animals, and remember the sample’s exact location until the sample can be collected without disturbing the animals or putting researchers at risk. Sample collection and storage must also follow specific protocols to preserve the data of interest (e.g. pathogen presence, DNA content, hormonal compounds, dietary components). This project will focus on (i) developing datasets and protocols to facilitate autonomous detection of defecation events and collection of faecal samples by terrestrial and aerial robotic systems (in collaboration with DC1 and DC3), and (ii) using drone-based behavioural observation and faecal samples to understand the role of mixed-species groups in mediating disease transmission within savanna ungulate communities, and between wild and domestic ungulate populations. 

Host institution: Max-Plack-Gesellschaft (MPG), Germany

PhD enrolment: Konstanz University (UKON), Germany

Principal / Academic supervisor: B. Costelloe (MPG) / M. Wikelski (UKON)

DC 10: Whale tissue sampling and analysis

Aim: This PhD will develop a drone-based system to collect skin and blubber samples from marine mammals. The research should lead to a robotic-based approach that is less invasive than current methods, and which will allow sampling of more elusive species and individuals, and also allow sampling of animals from land. The DC will design, construct and test the system for use on bowhead whales in West Greenland. To avoid developing a tool that could potentially be used as a weapon, the mechanism will include the drone dropping a tag like device on the whale, which attaches to the body surface with suction cups. Once attached, the tag will collect a skin/blubber core sample from the whale, and then release itself. The detached tag will float up to the surface and be collected by the drone, using a custom made retrieval system. 

Host institution: Aarhus University (AU), Denmark

PhD enrolment: Aarhus University (AU), Denmark

Principal / Academic supervisor: F. Christiansen (AU) 

DC 11: Soundscape analysis for animal occurrence in harbour areas

Aim: This PhD project will investigate the occurrence of harbour porpoises in relation to the soundscape they are exposed to, using Autonomous Underwater Vehicle (AUV) systems that can simultaneously record the acoustic presence of harbour porpoises along with environmental data (e.g. ambient underwater noise). The focus of the project will be on behavioural patterns of the porpoises, such as their response to different noise levels, if they get displaced or habituated in the long term. Different sensors (e.g., fish echosounder and PAM device) will be combined to investigate, through soundscape analyses (DC6), the distribution/presence of harbour porpoises. Either a single AUV capable of carrying multiple sensors that do not interfere with each other or a synchronised fleet of AUV (DC8) carrying different sensors separately will be investigated. 

Host institution: BioConsult (BIOC), Germany

PhD enrolment: University of Southern Denmark (SDU), Denmark

Principal / Academic supervisor: G. S. Ham (BIOC) /  M. Wahlberg (SDU)

DC 12: Interpretation of large-scale drone-based eDNA sampling

Aim: Large-scale drone-based eDNA sampling (DC3) has the potential to provide data from localities that are not reached otherwise without disturbance, such as the centers of water bodies, or from the tree canopy, sampling multiple sites in single campaigns, also in remote areas. In close collaboration with DC3-4-7, this project will iteratively determine needs for end-users, as well as collect eDNA-samples, with initial tests and developments carried out in southern Germany, targeting Lake Constance, and smaller lakes and their catchments in the region. These lakes are already monitored closely with traditional methods and eDNA sampling has also been carried out. They thus provide an ideal case study against which to evaluate the drone-sampled eDNA data for further routine applications. In a final stage of the project (year 3), the optimised system will be deployed to the other, more remote study sites of the consortium to evaluate its global use.

Host institution: Konstanz University (UKON), Germany

PhD enrolment: Konstanz University (UKON), Germany

Principal / Academic supervisor: L. Epp (UKON)