PhD students

Philip Alexander Hedlund Smith 

Title of the PhD project
Big data analytics to support ecosystem-based risk management of marine ecosystems

Supervisors
Patrizio Mariani, Asbjørn Christensen and Michael St. John

Background of the project
Ocean dynamics are essential for the functioning of the Earth system with important effects on climate regulation and global biodiversity. Regional and global processes driving storage and transport of heat, carbon, nutrients, and marine organisms are crucial for providing many ecosystems’ goods and services that enable life on Earth. These processes are driven by mechanisms interacting and operating over wide ranges of spatial and temporal scales, and inherently involve both horizontal and vertical dimensions, making them exceedingly difficult to monitor and to understand fully.

About the project
The general objective is to determine and understand spatio-temporal dependencies, relations, and mutual effects in the abundant climate and biogeochemical data. The goal is to understand these relationships as well as constructing frameworks for predicting future behavior. Moreover, to establish systems where ocean and ecosystem dynamics are learned and can be emulated for different initial state values. Neural networks and deep learning approaches in particular display major advantages in exploiting spatio-temporal data and capturing nonlinear relations in data compared to classical approaches.

Perspectives
Generating deep learning frameworks to combine remotely sensed and in situ observations may improve estimates and models of subsurface ocean state variables, which presently can be difficult to monitor due to the scarcity of local measurements. Furthermore, predictive data-driven models that accurately reproduce simulation data may facilitate comprehensive risk analyses and assessments, as changes in simulation data for varying driver inputs may be considerably less time consuming.

David Dylan O'Brien-Møller

Title of the PhD project
Building the coastal ARGO (ARGO+)

Supervisors
Patrizio Mariani and Colin Stedmon, DTU Aqua & Roberto Galeazzi, DTU Electro

Background of the project
Argo floats are autonomous, freely drifting profiling instruments capable of adjusting their buoyancy to move vertically in the water column. Typically deployed in open ocean environments for periods of 4-6 years, Argo floats operate by drifting at approximately 1000 meters depth for about 10 days before descending to 2000 meters. From there, they ascend toward the surface, collecting vital oceanographic data such as temperature and salinity. Upon surfacing, they transmit collected data via satellite. Over the past two decades, thousands of Argo floats have revolutionized ocean data collection, significantly enhancing our understanding of ocean dynamics and climate processes.

About the project
Despite their widespread use and success, Argo floats face limitations in specific marine environments. In areas with strong currents, Argo floats are quickly displaced from regions of scientific interest. Additionally, in shallow coastal areas, these floats risk being driven ashore or are unable to perform their standard deep-water profiling cycles. This project aims to develop technology to enable Argo floats to operate in coastal areas through developing methods for Argo floats to control their position. To take advantage of the new areas open to Argo floats, novel sensor technology will be integrated into Argo floats, enabling collection of new types of oceanographic data previously unavailable in these regions.

Perspectives
Increasing the types of data that can be collected by Argo floats and increasing the area in which they can operate in, especially in coastal areas, will yield critical data from currently under-sampled coastal regions. Ultimately, these advancements will improve our understanding of coastal marine ecosystems, ocean processes, and human impacts on these vital environments.

Previous PhD students within the research area Observation Technology

Aurelia Pereira Gabellini
Ecological connectivity in the Atlantic Ocean: Past, present and future (link to thesis awaits publication)

Anshul Chauhan
Resolving marine ecosystem dynamics in time and space with machine learning approaches

Søren Lorenzen Post
Blue whiting (Micromesistius poutassou): behaviour and distribution in Greenland waters