Doctoral researchers
Digital Waters doctoral education pilot includes over 60 doctoral researchers across the partner Universities, in collaboration with Finnish Research Institutes and Industry.
DIWA doctoral education pilot doctoral researchers are driving change in the water sector. They will be equipped with methodological, data, and management skills so that they will become the game changers in the field of water research. governance and innovation.
Next generation of experts
Over 60 doctoral researchers
Across the partner Universities, in collaboration with Finnish Research Institutes and Industry.
Over 15 different countries
DIWA PhD Pilot Doctoral researchers come from all over europe and beyond.
Multidisciplinary research
PhD research topics range from environmental law and policy to robotics, water management, ecology, hydrology, engineering and biology.
Browse doctoral researchers by University
University of Oulu
Ashutosh Taral
My doctoral research focuses on snow process modelling in Finland using SnowModel and remote sensing to analyze snow cover area, snow water equivalent (SWE), snowmelt timing, and density dynamics. By integrating in-situ and satellite datasets, I aim to improve hydrological forecasting, assess climate change impacts, and enhance microwave SWE retrievals—contributing valuable insights for climate adaptation and water resource management in boreal and subarctic environments.
Christoph Gocht
My doctoral research applies the hydro-social cycle as a theoretical framework and will include qualitative data to better understand what transformative urban water management means and encompasses, and to gain insights into the interrelationships between water, water engineering & technology, governance, and urban populations. The aim is to soften rigid governance structures to increase the resilience of urban populations to future extreme events, and ultimately to contribute to transformative change in water management.
Eeva Järvi-Laturi
My research focuses on the relationship between peatland vegetation and methane emissions in northern fens. By studying fine-scale variations through fieldwork, I aim to identify factors regulating methane fluxes measured with manual/automatic chambers and Eddy Covariance towers. Combining these data with ecohydrological analysis, remote sensing, and climate/ecosystem models, my goal is to specify and upscale local flux predictions, contributing to climate change and global carbon cycle research.
Elina Niemelä
My doctoral research examines land use impacts on iron leaching and iron retention in nature-based water treatment structures. Measurements in Oulu and Environmental Administration data are used to examine links between iron concentration and land use (e.g., peatland forestry, drainage in urban areas) in areas such as black schist and acid sulfate soils. The goal is to deepen understanding of iron dynamics to prevent leaching and identify effective methods to retain iron.
Elli-Noora Savonen
My doctoral research focuses on environmental and technical factors affecting the occurrence of Legionella bacteria in built and natural aquatic environments, with a special attention to groundwater-surface water interactions. I utilise data from Finnish Legionnaire’s disease case studies over a 10-year period, meteorological data from the national database, and results of groundwater samples. Information provided by this research can help to improve the management of Legionella risk in the water supply.
Farid Mousavi
My Doctoral research mostly involves digital twins and AI in water resources, especially in flood management. My focus is mainly on anomaly detection and physics-informed machine learning to create models that are not only accurate but also fast and flexible. In parallel, I aim to use different frameworks to analyze the socio-economic aspects of flood management using green infrastructures and help the government to encourage people to use these tools. Also, I like to use an AI model for this objective.
Getnet Demil
My doctoral research focuses on AI-driven snow hydrology, integrating remote sensing, image processing, and deep learning to estimate snow water equivalent (SWE), snow cover fraction (SCF), snow depth, and snow extent. By combining satellite data, UAV imagery, and field measurements, I develop advanced machine learning models to enhance spatial resolution and improve hydrological forecasting, contributing to climate adaptation and sustainable water resource management in snow-dominated regions.
Henna Pääkkö
My PhD research examines how land use intensification impacts freshwater biodiversity across Nordic (Finland, Sweden, Norway) ecosystems. Using long-term monitoring data, predictive modeling, and gradient forest analysis, I assess changes in benthic invertebrate communities at taxonomic and functional levels. This work identifies ecological thresholds and informs sustainable land use and freshwater conservation strategies under climate and environmental change.
Hung Bui
My doctoral research focuses on automated quality control of online water monitoring data using a hybrid statistical and machine learning approach. The research method is mixed quantitative and qualitative. The developed tool could improve the reliability and availability of sensor data, thereby reducing unnecessary maintenance visits and supporting predictive modeling and real-time decision-making.
Janne Torvela
My doctoral research focuses on the application of customizable hardware in field and laboratory research, for concept testing and prototyping in the context of agricultural and environmental research. This is done through case examples. The aim is to lower the barriers for access to technology for researchers looking for custom solutions and the use of modern IoT technologies for field research.
Jiahui Qiu
My PhD research advances real-time ice prediction by integrating multi-source remote sensing, in-situ monitoring, and AI-driven digital twin models. This work enhances river ice mapping, phenology tracking, and flood forecasting in cold regions, offering a novel approach using lake ice as a proxy. It supports climate resilience and environmental monitoring in sub-Arctic hydrology.
Jonna Tauriainen
My research focuses on water-carbon interactions and their effects in shaping aquatic microbial processes and greenhouse gas dynamics in northern streams and lakes. I’m combining temporal and spatial 16S rRNA microbial community data with high-frequency hydrochemical and greenhouse gas measurements. The results will enhance our understanding of the carbon dynamics in northern catchments amidst unprecedented change, and the role aquatic microbes play in these processes.
Milad Anboohi
My doctoral research focuses on snow remote sensing and coupled snow–hydrology modeling to enhance runoff simulations under climate change. Using satellite data, SnowModel, and a hydrological model, I assess snowmelt-driven impacts on water availability, hydropower, and flood risks. By integrating calibration, assimilation, and scenario analysis, my work advances predictive accuracy and supports climate-resilient water management strategies.
Parsa Parvizi
My doctoral research explores spatial sub-arctic ecohydrology in the Lompolonjängänoja headwater catchment of northern Finland, integrating high-resolution distributed temperature sensing (DTS) analysis and the SpaFHy model to simulate lateral water flows. To understand hydrological responses to climate variability, the study uses the HydroGeoSphere (HGS) for surface-subsurface hydrological simulations and aims to explore how climate variability impacts runoff generation mechanisms, dissolved organic carbon (DOC) transport, and hydrological connectivity.
Pierre Jaouen
My doctoral research focuses on automated and controlled subsurface drainage and subirrigation. I research the structure of a suitable 1D short-term predictive physical model for groundwater in drained fields. I investigate methods for system identification and parameter estimation that can be used to calibrate the model automatically for different fields. The model and identification method are used for model-based control of controlled drainage wells and validated with tests in fields.
Pietari Pöykkö
My research focuses on spatiotemporal analysis of groundwater hydrology and hydrological regime in Finland, using a 50-year national dataset. Analyzed factors impacting groundwater are 1) changes in climatic conditions (precipitation, temperature, evapotranspiration) and 2) land use changes (forestry, agriculture, land abstraction, ditching). Research investigates the conditions leading to groundwater droughts and assesses the suitability of hydrological indices as proxies for drought prediction in Finnish conditions.
Raffa Ahmed
My PhD research focuses on enhancing the resilience of hydropower systems in the Oulujoki basin under current and future climate change scenarios. I investigate the impacts of changing flow regimes and evolving water-energy trade-offs by applying hydrological modeling, spatial analysis, and multi-objective optimization. This work contributes to the development of adaptive hydropower management strategies that promote climate resilience, support long-term energy planning, and ensure sustainable operation of the Oulujoki hydropower system.
Robel Bekele
My doctoral research examines the load and fate of sanitation-derived emerging contaminants in freshwater systems, focusing on their implications for water supply. I utilize data extraction from national databases, laboratory analyses, and field studies to assess the occurrence of contaminants in freshwater environments. This interdisciplinary approach informs water resource management strategies, addressing environmental and public health concerns while supporting policies for sustainable sanitation practices and water quality protection.
Sajjad Mohammadzade Vatanchi
My doctoral research focuses on integrated hydrological and energy systems modeling, emphasizing short-term hydropower regulation under climate change. I use AI-based inflow forecasting, optimization tools, and scenario analysis to assess system resilience. Combining quantitative modeling with interdisciplinary insights, my work supports sustainable energy planning and adaptive water management by linking hydrology, energy markets, and policy-relevant outcomes.
Simo Ylönen
My doctoral research focuses on applying modern artificial neural network methods to national hydrological monitoring networks in the Northern Hemisphere by using combinations of multiple machine learning models. The aim is to implement proven, multiverse-tested approaches to various hydrological challenges, such as flood and snow impact forecasting, as well as robust real-time quality control of hydrological sensor-level data.
Yanni Yang
My doctoral research is centered on site-specific groundwater (GW) modeling, interactions between GW and surface water, and dissolved carbon transport at the Oulanka Research Station, Finland. I systematically document and map the hydrogeology and geological structure of the Oulanka aquifer using GW and water quality data, subsequently developing a conceptual model with MODFLOW. The PCA and HCA statistical analyses will be employed to analyze spatiotemporal variability in GW geochemistry. A further objective is to construct a high-resolution 3D model utilizing either the GMS MODFLOW package or the Amanzi-ATS software.
University of Turku
Amirhossein Nourian
This research is centered on the development of resilient, energy-efficient robots designed to thrive in unstructured environments through adaptive autonomy and optimized resource management. Deep reinforcement learning policies will be trained in high-fidelity simulations and seamlessly transferred to real-world platforms, enabling prolonged data-gathering and surveillance missions. Rigorous sim-to-real validation will be conducted, and my expertise in DRL agent design and robotics will guide every stage of implementation.
Beata Plutova
My PhD research explores the combined effects of climate change and land use on the water quality of rivers. The research combines statistical analysis and modelling to identify pollution hotspots and analyze seasonal, spatial, and temporal trends. This work provides actionable insights for sustainable water management practices and climate change adaptation strategies.
Bilal Liaqat
My doctoral research leverages Explainable Artificial Intelligence (XAI), Machine Learning, and Big Data Analytics to predict snowmelt-driven streamflow and ice cover dynamics in Finnish rivers. Integrating advanced Deep Learning algorithms like LSTM with historical data, geospatial analysis, and field measurements, I enhance flood forecasting and deliver interpretable insights into hydrological processes. This work advances water resource management, ecosystem resilience, and climate change adaptation through robust AI-driven analytics.
Emmy Kärkkäinen
My doctoral research focuses on identifying Atlantic salmon spawning areas in the Tana River using multispectral airborne laser scanning (ALS), 2D hydraulic modeling, aerial photographs, and orthoimagery. By integrating an ALS-derived, high-resolution digital elevation model (DEM) with hydraulic data, I simulate the effects of varying discharges and water levels on the location and size of spawning areas. I aim to develop a quantitative and objective approach to underwater habitat mapping.
Farhan Humayun
My research lies at the intersection of Remote Sensing, Machine Learning and Computer Vision. The primary objective of my research work is to develop novel techniques for efficient multi-modal data and sensor fusion using state-of-the-art, deep learning based approaches enabling a more comprehensive understanding of the environment under study. Examples include measurement, perception and vision data from satellites, UAVs, UGVs and ground based fixed sensors.
Haizhou Zhang
My research focuses on multi-robot collaboration, integrating UAVs and USVs with LiDAR, cameras, and IMUs for enhanced situational awareness in environmental monitoring. Using deep learning for keypoint extraction, segmentation, and secure data sharing, I aim to develop a resilient sensor fusion framework and validate it through field experiments in Finnish rivers.
Iiro Seppä
My doctoral research focuses on improving Deep Learning rainfall-runoff predictions in ungauged basins (PUB) using LSTM networks and understanding their inner workings better. This can improve flood and water quality forecasts and help optimize hydropower generation. I am also creating a large sample hydrometeorological dataset for Finnish catchments (CAMELS-FI) that is suitable for deep learning, but also various other types of hydrological research.
Laura Laaksonen
The aim of my PhD project is to study the fate of microplastics and nutrients in the marine environment in Finnish coastal waters. I’m going to research the impact of estuary-to-sea processes on transport and retention of pollutants in the Baltic Sea by analyzing the compositions of sediment cores, sediment traps, and bulk samples from sandy beaches, and also the pollutant load in the different depths of the water column within the estuary.
Lukas Zehner
My PhD research explores how Mechanical Engineering can support water analysis in Finland through semi-autonomous monitoring. It focuses on Unmanned Aerial-Aquatic Vehicles (UAAVs), aiming to unify aerial and underwater capabilities. Key challenges include propulsion design, hydrostatics (e.g., ballast use), sensor optimisation, and waterproofing. This addresses a current gap in robotic systems for large-scale aquatic monitoring.
Luma Fonseca Alves
My research aims to harness the potential of digital tools to collaborative design urban Nature-Based Solutions (NBS) scenarios, enabling stakeholders to assess and develop scenarios within innovative digital environments, while engaging stakeholders in reflecting upon the impacts of their decisions. This research will help to bridge the often technocratic process of urban NBS planning with a more accessible, collaborative, and co-creative methodology, fostering informed and inclusive sustainable urban systems.
Mariah Josten
My research explores how ferry-generated wake waves in the Archipelago Sea resuspend sediment, alter flow velocities, and cause water-level fluctuations. The study combines multivariate statistical analysis for site selection, field measurements of flow and sediment dynamics, and hydrodynamic modeling for complex coastal environments. This work provides new insights into coastal processes to support more sustainable ferry operations and coastal planning.
Sampo Soini
I study the internal structure and groundwater reserve of an ice-marginal delta complex in southern Finland. The methods applied in this research range from sedimentological logging and soil sample analysis to ground penetrating radar surveying and 3D-modelling. Additionally, terrestrial LiDAR scanning and seismic surveying are conducted. By using systematic digitisation, the research aims to optimise the management of nationally important groundwater reserves more sustainably.
Sier Ha
My doctoral research focuses on robust, lightweight localization algorithms for UAVs and USVs in remote environments. Leveraging LiDAR and multi-sensor fusion, I develop GNSS-enhancing solutions that support autonomous missions where GNSS is unreliable. The work emphasizes end-to-end, cross-modal integration to enable real-time, efficient onboard deployment in resource-constrained robotic systems.
Sopitta Thurachen
My research focuses on utilizing deep learning frameworks that enhance the adaptability of multispectral point cloud mapping in unseen environments, using techniques such as weakly/self-supervised methods and few-shot/zero-shot learning. With these frameworks, my goal is to develop robust mapping algorithms that can generalize effectively without requiring extensive ground-truth annotations. Another focus of my research is developing deep learning algorithms for real-time data processing, detection, and segmentation.
Tanya Santalahti
My research focuses on the effects of climate change and land use on nutrient loads from arable lands. Utilizing both social and technical approaches, including long-term monitoring data, interviews, and survey data, I assess the impact of summer droughts on nutrient loads from clayey soils and the scalability of water protection measures in agriculture. These studies aim to support climate resilience and drive systemic change in food systems.
Waqar Khalid
My doctoral research focuses on Fluvial Geomorphology, studying how rivers evolve over time under natural and anthropogenic pressures. I monitor river changes using remote sensing, hydrological modelling, and fieldwork using terrestrial, airborne, and river-echo sensors. I aim to develop innovative, data-driven approaches for understanding and predicting river dynamics by combining existing and newly collected data.
Aalto University
Alexis Awaitey
As part of the digital twin construction for the wastewater treatment facility in the Helsinki region, my research focuses on the model prediction improvement of nitrous oxide emissions. Nitrous oxide is a greenhouse gas ~300 times stronger than carbon dioxide, and the goal of the study is to gain more understanding of the conditions in which it is emitted to further improve emission forecasting in the digital twin.
Anna Vilhunen
In my doctoral research, I am examining doctoral supervision with a special focus on inclusivity. I utilize a mixed-methods approach to examine both engineering doctoral supervisors’ and supervisees’ experiences and conceptions.
Bhattarabhop Viriyaroj
I am studying the global food system while trying to find the characteristics of the system across the globe using archetype analysis with machine learning through the perspective of systems thinking. The research will be focusing on the crop production, livestock, and aquaculture by looking at various aspects of the systems, including biophysical, socio-economic, and management variables, using historical until the present data to find characteristics of the change from the past as well as the present characteristics of the system.
Dané Smith
I study political and technical interactions in transboundary water governance and water diplomacy. Dimensions of my research include examining processes, power and meaning in these political-technical interactions in transboundary governance settings. Adopting the view that transboundary water governance and water diplomacy exist in complexity, qualitative approaches are largely used inductively and deductively to better understand where political-technical linkages enable or hinder capacity for transformation.
Emil Karlsson
In my doctoral research, I focus on enhancing the resilience of water infrastructure against cyberattacks and other disturbances. I utilize IEC 61499, digital twins, and artificial intelligence to develop anomaly detection methods for these systems to detect, adapt and recover from attacks.
Henri Heiskanen
My doctoral research explores the potential of close-range remote sensing systems to enhance river flow and water quality monitoring in seasonally ice-covered rivers, addressing future needs in water resources management, digitalization, and environmental modeling. The work integrates novel sensor technology, field measurements, digital image processing, computer vision, and artificial intelligence to detect and validate the physical and chemical characteristics of flowing river water.
Jan Olsman
My study focuses on the development of analysis methods to forecast discharge and material transport in seasonally ice-covered rivers. The new analysis method, based on machine learning techniques, includes data from the vertical (critical zone) and horizontal (river catchment) layers. The purpose of this study is to gain continuous river information based on limited online data. Finally, this new method is to be applied through data pipelines into watershed digital twins.
Jeroen Poelert
My research project focuses on finding pathways to reduce the environmental impact of our present food system from global to farm scale, with a particular focus on nutrient circularity and crop-livestock systems. The studies include both technical and social research approaches, like food systems modelling, participatory backcasting with farms, and futures studies.
Seyed Mahdi Jafari Mohammadi
As a doctoral researcher, I investigate flow–vegetation–sediment interactions across scales through flume experiments, field observations, and hydrodynamic modeling, aiming to enhance flood prediction, sediment management, and habitat restoration in river–floodplain systems.
Mohammadreza Hassani
Cities have forgotten how to dance with rain as natural pervious earth was replaced by impervious areas that can turn rainfall into polluted floods. To address this, my doctoral research focuses on evaluating and modeling how urbanization affects stormwater quality and quantity and then using green infrastructures to restore natural urban hydrology. By applying simulation and optimization methods, we will determine where and which stormwater management solutions work best and can help cities and nature thrive together once again.
Reeta Vaahtera
In my doctoral research, I study environmental changes in high-latitude rivers, specifically focusing on ice-covered rivers in subarctic environments. My work combines field measurements, flume experiments, and modeling approaches to improve our understanding of ice-covered flow under climate change.
Seyed Hosseini
How does water balance change in agricultural areas? – This is the focus of my doctoral research. I am exploring the interactions between water balance components within agricultural fields and their surrounding environments. By integrating deep learning models with hydrological modeling, I aim to better understand these relationships and improve water management strategies in agricultural landscapes.
Tiia Westerberg
My doctoral research consists of identifying the issues in current natural resources governance with a special focus on water management. I look at it from the point of stakeholder representation, knowledge coproduction, and decision-making in the (in)formal processes, with a holistic approach aiming towards solutions that are socially and ecologically sustainable and equitable. My work will mostly include qualitative methods such as interviews and different policy and documentation analyses.
Tuomas Haapala
My doctoral research concerns the integration of open hydrological data into the current generation of hydrological models, the development of these models to consider climate change-induced developments in hydrology, and the construction of a digital twin framework utilizing open data to produce hydrological model simulations.
Åbo Akademi
Alexandra Nyberg
My research focuses on the geochemical effects of gypsum treatment on different types of fine-grained farmland soils in Finland. Soil geochemistry will be combined with water geochemistry, both in field and laboratory conditions. The aim is to assess how gypsum influences the transport and mobility of sulfur, nutrients, and metals through the water flow path from the topsoil to the subsoil, and ultimately to the recipient stream over time.
Dimitrios Kampantais
I will assess the impact of ship-induced waves on biodiversity, community structure, function, and predator-prey dynamics of invertebrates in Fucus vesiculosus belts on Baltic Sea rocky shores and clarify the ecological consequences of this anthropogenic stressor on the marine food webs. Wave intensity, light penetration, and sedimentation will be monitored using loggers at coastal sites along the ferry route, where algal and faunal samples will be collected for laboratory analysis.
Petra Saari
I study how ferry waves affect bladderwrack, one of the key species of the Baltic Sea. It is known that in soft sediments, waves cause erosion, but hard bottom habitats remain less studied. Some suggest negative impacts through increased turbidity, whereas others have found positive impacts through decreased sedimentation. I will deploy loggers to measure flow velocity, light, and sedimentation, and take algae samples by scuba diving for laboratory analyses.
University of Eastern Finland
Dilshan Dissanayakage
Micro- and nanoplastics, including polystyrene, pose significant ecological risks. While nanoparticle toxicity in freshwater fish is increasingly studied, effects related to surface charge remain underexplored. My research investigates how positively and negatively charged polystyrene nanoparticles (PS-NPs) affect reproduction, behavior, and genotoxicity in European whitefish. Using ICP-MS for particle localization and RT-PCR for embryotoxicity and genotoxicity, I aim to reveal surface charge- and concentration-dependent toxicity of PS-NPs.
Elina Heikkilä
My doctoral research investigates the relationship between legal decision-making and scientific knowledge within the framework of the EU Water Framework Directive. Through bridging the gap between legal obligations and scientific knowledge, this study seeks to contribute to the realization of the WFD’s environmental objectives. Methodologically, the research uses legal doctrinal analysis complemented by relevant theoretical frameworks to critically assess and contextualize this interface.
Marika Teini
My doctoral research aims to systematize and analyze Finnish and EU legislation on small water bodies and headwaters. By combining legal doctrinal analyses with empirical research and regulation theory, I seek to enhance understanding of how legislation impacts biodiversity, water quality, and larger water ecosystem health, providing insights for better environmental governance.
Mehran mahdian
My doctoral studies focus on enhancing water quality and biological simulations in boreal and Arctic regions. In this research, we propose a framework that combines numerical and machine-learning tools with observational datasets to estimate fish and zooplankton biomass in lakes. The results will provide valuable information for preserving the biodiversity and ecological integrity of boreal ecosystems.
Mikhail Sandzhiev
My research develops real-time monitoring of metal contamination in environmental waters using portable X-ray fluorescence (p-XRF) technology combined with GIS mapping. Through field sampling, lab validation, and quantitative data analysis, I provide rapid spatial visualization of metal pollutants, enhancing proactive environmental management and sustainable ecological monitoring. This approach significantly reduces response time to contamination events, safeguarding ecosystems and public health effectively.
In collaboration with
Ministry of Education and Culture
DIWA Flagship is funded by the Finnish Ministry of Education and Culture directly and through the Research Council of Finland.