Climate AI Nordics

Climate AI Nordics

Nordic network for collaborations around tackling climate change using AI and machine learning.

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events

Self-supervised pre-training for glacier calving front extraction from synthetic aperture radar imagery

Event date:

Webinar with Nora Gourmelon, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The factors influencing melt at glacier fronts facing the ocean remain an active area of research. To better understand these factors, it is essential to monitor changes in glacier calving fronts. Due to the importance of weather-dependent and seasonal variations, Synthetic Aperture Radar (SAR) is the preferred imaging method for such monitoring. In recent years, deep learning models have been developed to automate the extraction of calving front positions, however, their performance on SAR data remains suboptimal. Limited labeled data and high variability in SAR images hinder traditional supervised learning. Foundation models pre-trained on large, diverse datasets could provide robust feature representations that require minimal labeled data for fine-tuning on specific SAR tasks. However, in preliminary experiments, we found that the domain gap is too large for the task of extracting calving fronts from SAR imagery, and foundation models fine-tuned on the "CAlving Fronts and where to Find thEm" (CaFFe) benchmark dataset performed subpar. Therefore, we compiled an unlabeled dataset of Sentinel-1 SAR image sequences of Arctic glaciers, each associated with a single Sentinel-2 optical reference image. Using this dataset, we developed a novel multi-modal self-supervised pre-training strategy and applied it to pre-train a hybrid CNN-transformer model. Fine-tuning on the CaFFe benchmark showed that the pre-trained model outperforms its non-pre-trained counterpart, enabling more robust calving front segmentation and demonstrating the potential of data-efficient learning for SAR imagery.

Earth observation and deep learning for urban applications

Event date:

Webinar with Sebastian Hafner, RISE. Urbanization is progressing at an unprecedented rate in many places around the world, making Earth observation (EO) a vital tool for monitoring its dynamics on a global scale. Modern satellite missions provide new opportunities for urban mapping and change detection (CD) through high-resolution imagery. At the same time, EO data analysis has advanced from traditional machine learning approaches to deep learning (DL), particularly Convolutional Neural Networks (ConvNets). Yet, current DL methods for urban mapping and CD face key challenges, including effectively integrating multi-modal EO data, reducing reliance on large labeled datasets, and improving transferability across geographic regions. This talk will present methods to address these challenges in urban mapping and CD, with applications such as multi-hazard building damage detection. It will also highlight the IDEAMAPS Data Ecosystem project, showing how its community-centric approach advances our understanding of urban deprivation and enables DL-based deprivation mapping.

International Conference: Climate Impacts in a Changing World 2026

Event date:

The Swedish Centre for Impacts of Climate Extremes (CLIMES) invites abstract submissions for the international conference Climate Impacts in a Changing World 2026, held in Uppsala on March 9–11, 2026. The event fosters interdisciplinary dialogue on the wide-ranging impacts of climate extremes on human and natural systems.

Workshop on AI for Climate and Conservation (AICC) at EurIPS 2025

Event date:

Climate AI Nordics is excited to announce that the [Workshop on AI for Climate and Conservation (AICC)](https://sites.google.com/g.harvard.edu/aicceurips) has been accepted for [EurIPS 2025](https://eurips.cc/workshops/)! The AICC workshop will take place in Copenhagen, Denmark, Dec 6th or 7th (TBD; workshops are co-located with EurIPS)!

Hyperparameter tuning, Bayesian optimisation and applications related to climate change

Event date:

Webinar with Sigrid Passano Hellan, NORCE Norwegian Research Centre. If you don’t want to be setting and tuning machine learning model hyperparameters such as the learning rate yourself, then hyperparameter tuning is a good alternative. Also called hyperparameter optimisation, it has been developed to reduce the amount of machine learning experience required to set up a model by automatically finding the best hyperparameters. By reducing these hurdles to adoption, machine learning can be adopted for more climate applications. Bayesian optimisation is a popular method for hyperparameter optimisation, and consists of fitting a probabilistic model and using that to inform the optimisation process. Both hyperparameter optimisation and Bayesian optimisation can be greatly enhanced using transfer learning, where we learn from previous optimisation problems to speed up the current one. In this talk I will give an introduction to methods for hyperparameter tuning and Bayesian optimisation, and how transfer learning can be used. Then I will present how Bayesian optimisation has been applied to climate related research, as we discussed in a recent survey. We found the main application areas to be material discovery – finding better materials for solar panels; wind farm layouts – deciding where to place turbines; optimal control of renewables – e.g. adjusting wind turbine blade angles to current conditions; and environmental monitoring – e.g. finding pollution maxima.

Short course on artificial intelligence for environmental data

Event date:

This two-day short course introduces early-career researchers and professionals to how AI can be applied for processing and interpreting environmental data, with a focus on climate-related analysis and earth observation. Participants will learn core AI methods—including Large Language Models (LLMs)—and gain hands-on experience with practical tools through guided exercises.

SWEO2025: Earth observation event in Stockholm

Event date:

SWEO2025 is an event that revolves around the practical applications of Earth observation data, including within environmental monitoring and similar areas. The event takes place in Solna, Stockholm, between October 23rd and 24th. Click [here](https://jirango.com/cview/web/10596f6a) for registration to the event.

Self-supervised pre-training for glacier calving front extraction from synthetic aperture radar imagery

Event date:

Webinar with Nora Gourmelon, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The factors influencing melt at glacier fronts facing the ocean remain an active area of research. To better understand these factors, it is essential to monitor changes in glacier calving fronts. Due to the importance of weather-dependent and seasonal variations, Synthetic Aperture Radar (SAR) is the preferred imaging method for such monitoring. In recent years, deep learning models have been developed to automate the extraction of calving front positions, however, their performance on SAR data remains suboptimal. Limited labeled data and high variability in SAR images hinder traditional supervised learning. Foundation models pre-trained on large, diverse datasets could provide robust feature representations that require minimal labeled data for fine-tuning on specific SAR tasks. However, in preliminary experiments, we found that the domain gap is too large for the task of extracting calving fronts from SAR imagery, and foundation models fine-tuned on the "CAlving Fronts and where to Find thEm" (CaFFe) benchmark dataset performed subpar. Therefore, we compiled an unlabeled dataset of Sentinel-1 SAR image sequences of Arctic glaciers, each associated with a single Sentinel-2 optical reference image. Using this dataset, we developed a novel multi-modal self-supervised pre-training strategy and applied it to pre-train a hybrid CNN-transformer model. Fine-tuning on the CaFFe benchmark showed that the pre-trained model outperforms its non-pre-trained counterpart, enabling more robust calving front segmentation and demonstrating the potential of data-efficient learning for SAR imagery.

Generative domain adaptation and foundation models for robust Earth observation

Event date:

Webinar with Georges Le Bellier, CNAM. Deep learning for remote sensing plays a crucial role in turning satellite and aerial imagery into dependable, real-world insights. However, Earth observation models must handle diverse environments, sensors, and conditions—such as clouds, seasonal shifts, and geographic differences—while still producing accurate results. In this talk, we explore two paths that lead to more robust and adaptable algorithms: generative domain adaptation and geospatial foundation models. First, I will introduce FlowEO, a generative approach of Unsupervised Domain Adaptation (UDA) for Earth observation, and show its high performance in UDA scenarios for several downstream tasks, including dense prediction and classification. This flow-matching-based translation method improves pretrained predictive models' accuracies in challenging scenarios such as post-disaster response and high cloud coverage cases with SAR-to-optical translation. FlowEO’s generative domain adaptation method is independent of the downstream task and does not require retraining the predictive model. Then, I will present “PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models“, a standardized evaluation protocol that covers a diverse set of datasets, dense prediction tasks, resolutions, sensor modalities, and temporalities. This benchmark includes comparison between geospatial foundation models but also with supervised baselines, namely U-Net and ViT, and highlights the strengths and weaknesses of GFMs. In addition, PANGAEA evaluates models’ accuracy in cases where labels are limited and questions the impact of multi-temporal data for GFMs.

ESA Phi-Lab Sweden Opening Event

Event date:

Welcome to the official opening of ESA Phi-Lab Sweden in Stockholm. The opening ceremony will take place on August 26, starting with a presentation of the Phi-Lab’s mission and thematic focus, followed by a symbolic ribbon-cutting ceremony. After a light lunch, the afternoon continues with a conference programme featuring inspiring presentations, forward-looking discussions, and a poster session for sharing contributions (pre-registration required). We welcome researchers, start-ups, industry players and the public sector to join this important step towards a future where Sweden plays an active role in shaping AI-driven space applications.

Debiasing AI predictions for causal inference without fresh ground truth data

Event date:

Webinar with Markus Pettersson, Chalmers University of Technology. Machine learning models trained on Earth observation data, particularly satellite imagery, have recently shown impressive performance in predicting household-level wealth indices, potentially addressing chronic data scarcity in global development research. While these predictions exhibit strong predictive power, they inherently suffer from shrinkage toward the mean, resulting in attenuated estimates of causal treatment effects and thus limiting their utility in policy evaluations. Existing debiasing methods, such as Prediction-Powered Inference (PPI), require additional fresh ground-truth data at the downstream causal inference stage, severely restricting their applicability in data-poor environments. In this paper, we introduce and rigorously evaluate two novel correction methods—linear calibration correction and Tweedie's correction—that substantially reduce prediction bias without relying on newly collected labeled data. Our methods operate on out-of-sample predictions from pre-trained models, treating these models as black-box functions. Linear calibration corrects bias through a straightforward linear transformation derived from held-out calibration data, while Tweedie's correction leverages empirical Bayes principles to directly address shrinkage-induced biases by exploiting score functions derived from predicted outcomes. Through analytical exercises and experiments using Demographic and Health Survey (DHS) data, we demonstrate that both proposed methods outperform existing data-free approaches, can achieve significant reductions in attenuation bias and thus providing more accurate, actionable, and policy-relevant estimates. Our approach represents a generalizable, lightweight toolkit that enhances the reliability of causal inference when direct outcome measures are limited or unavailable.

High-stakes decisions from low-quality data: AI decision-making for conservation

Event date:

Webinar with Lily Xu, Columbia University. Like many of society's grand challenges, biodiversity conservation requires effectively allocating and managing our limited resources in the face of imperfect information. My research develops data-driven AI decision-making methods to do so, overcoming the messy data ubiquitous in these settings. Here, I’ll present technical advances in machine learning, reinforcement learning, and causal inference, addressing research questions that emerged from on-the-ground challenges in wildlife conservation. I’ll also discuss bridging the gap from research and practice, with anti-poaching field tests in Cambodia, field visits in Belize and Uganda, and large-scale deployment with SMART conservation software.

DIGeMERGE 2025 Conference

Event date:

DiGeMERGE 2025 is an international conference organized by researchers from the Digital Emergency Communication (DIGeMERGE) project. Funded under the CHANSE programme by the EU Commission (Horizon 2020) and Nordic research councils, DIGeMERGE explores the role of digital tools and platforms in public emergency communication across the Nordic countries. The conference provides a platform for researchers and students to present their work on the digital transformation of public emergency communication. Whether focusing on emergency management theories, risk analysis methods, or empirical case studies, participants will have the opportunity to share insights and engage with scholars from diverse disciplines, including the natural and social sciences.

The digital revolution of Earth system modelling

Event date:

Webinar with Peter Dueben, European Centre for Medium-Range Weather Forecasts. This talk will outline three revolutions that happened in Earth system modelling in the past decades. The quiet revolution has leveraged better observations and more compute power to allow for constant improvements of prediction quality of the last decades, the digital revolution has enabled us to perform km-scale simulations on modern supercomputers that further increase the quality of our models, and the machine learning revolution has now shown that machine-learned weather models are competitive with physics based weather models for many forecast scores while being easier, smaller and cheaper. This talk will summarize the past developments, explain current challenges and opportunities, and outline how the future of Earth system modelling will look like. In particular, regarding machine-learned foundation models in a physical domain such as Earth system modelling.

Efficient and precise annotation of local structures in data

Event date:

Webinar with John Martinsson, RISE and Lund University. Machine learning models now help scientists analyze vast datasets across every branch of science. These models typically improve with more data and larger architectures, mainly through supervised learning. Both training and evaluation therefore rely on labeled datasets. A main challenge is scaling the data labeling effort to the volumes required, because it is costly and label quality can vary. Methods that deliver inexpensive yet accurate labels are therefore essential. This talk examines how to lower annotation cost and increase label quality when labeling local structures in data—for example, a local structure can be a sound event in an audio recording. By detecting the boundaries of such structures automatically, we let annotators focus on supplying concise textual descriptions for the content within those boundaries. In this setting we analyze a widely used labeling method for audio where fixed and equal length audio segments are labeled with presence or absence of an event class. We benchmark it against an oracle method that defines an upper bound, and propose adaptive labeling techniques that achieve higher‑quality labels for the studied datasets at a lower cost.

SatML for large-scale above-ground biomass estimation

Event date:

Webinar with Ghjulia Sialelli, ETH Zurich. The combination of remote sensing and machine learning has made it possible to map forest properties at an unprecedented scale and resolution. In this presentation, I will focus on the application of deep learning techniques to estimate above-ground biomass (AGB), a key metric for tracking forest carbon and ecosystem dynamics. I will begin by introducing our recently published, machine-learning-ready dataset. It features high-resolution (10m) multi-modal satellite imagery, paired with AGB reference values from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission. Key aspects include the carefully selected geographic coverage, thoughtful integration of diverse satellite data sources, and the establishment of performance baselines using standard deep learning models. Next, I will describe our ongoing efforts to build on said baselines. This was done both through feature and model engineering. I will also mention some promising yet unsuccessful approaches, highlighting some key challenges of the task at hand. Finally, I will discuss future directions, including incorporating uncertainty estimation and exploring the potential for generating a global above-ground biomass map.

AI - a green gamechanger or a dirty conciousness?

Event date:

How can we utilize the enormous potential of artificial intelligence (AI) while also taking into account its impact on the climate? Artificial intelligence has the potential to transform large parts of our society and play a crucial role in the green transition. But at the same time, we must be aware of the energy consumption and the resource load that the use of AI also entails.

Optimising green infrastructure for climate-resilient cities: AI-driven approaches to urban cooling

Event date:

Webinar with Abdul Shaamala, Queensland University of Technology. Green infrastructure (GI) is critical in enhancing urban resilience, mitigating heat stress, and improving environmental sustainability. However, optimising the placement and configuration of green elements such as trees, parks, and vegetative corridors, requires a data-driven approach that accounts for microclimate variations, urban morphology, and long-term ecosystem benefits. This talk explores how artificial intelligence (AI), machine learning (ML), and geospatial analysis can be leveraged to optimise GI for urban cooling and climate adaptation. Specifically, it delves into tree optimisation strategies that enhance shade provision, reduce urban heat islands (UHI), and improve outdoor thermal comfort. By utilising computational models, including optimisation algorithms and thermal analysis, cities can strategically position vegetation to maximise cooling benefits while balancing urban development needs.

Reliable conclusions from remote sensing maps

Event date:

Webinar with Sherrie Wang, MIT. Remote sensing maps are used to estimate regression coefficients relating environmental variables, such as the effect of conservation zones on deforestation. However, the quality of map products varies, and -- because maps are outputs of complex machine learning algorithms that take in a variety of remotely sensed variables as inputs -- errors are difficult to characterize. Thus, population-level estimates from such maps may be biased. We show how a small amount of randomly sampled ground truth data can correct for bias in large-scale remote sensing map products. Applying our method across multiple remote sensing use cases in regression coefficient estimation, we find that it results in estimates that are (1) more reliable than using the map product as if it were 100% accurate and (2) have lower uncertainty than using only the ground truth and ignoring the map product. Paper: https://arxiv.org/abs/2407.13659

Machine learning for Earth system prediction and predictability

Event date:

Webinar with María J. Molina, University of Maryland. Machine learning can be used for Earth system prediction, or to study the uppermost limit of prediction skill theoretically achievable given the system's initial state or other factors, otherwise known as predictability. In traditional numerical weather prediction frameworks, we solve the governing partial differential equations starting from an initial state. This initialized prediction framework usually involves three stages: 1) generating the initial conditions of the Earth system, 2) running the mathematical representation of the system on a computer forward in time, and 3) analyzing the output and converting it into a format that is useful for end users. Machine learning can be used to improve each of these individual stages, or to circumvent the three stage framework altogether, and examples of each will be given in this seminar. More time during the seminar will be dedicated to the challenges surrounding subseasonal prediction, which focuses on lead times of three to four weeks, and how we can use machine learning to both uncover potential biases in our initialized prediction systems and how we can bias-correct them.

Edge AI and IoT-Driven Water Quality Monitoring

Event date:

Webinar with Atakan Aral, University of Vienna. The integration of Edge Computing and Artificial Intelligence (i.e., Edge AI) unlocks new possibilities for remote monitoring of water quality across diverse environments. This talk will explore the dual aspects of "remote" monitoring: (1) collecting water quality data in geographically remote regions with limited energy and connectivity and (2) using connected water quality sensors to gain real-time insights remotely. By deploying low-power or energy-harvesting sensors and learning from data in close proximity to sensors, we can improve efficiency, reduce latency, and maintain learning performance in challenging environments without reliable communication and energy infrastructures. The presentation will showcase two real-world case studies in river pollution source identification and monitoring of water distribution systems.

Tech-informed nature conservation - are we there yet?

Event date:

Webinar with Abdulhakim Abdi, Lund University. In the face of a global biodiversity crisis, the ability to efficiently monitor ecological change across space and time has never been more accessible. Advances in satellite remote sensing and AI have opened new frontiers for biodiversity monitoring by offering powerful tools to analyze vast datasets, automate species detection, and improve disturbance tracking. While these technologies hold great promise, their widespread adoption faces key challenges, including data accessibility, environmental costs, model interpretability, and the need for stronger interdisciplinary collaboration. This presentation explores the opportunities and limitations of tech-driven nature conservation, assessing whether current developments are sufficient to bridge critical knowledge gaps. The integration of remote sensing with AI brings us closer to a more data-informed approach to managing the living world - but are we truly ready to harness its full potential?

Leveraging AI for Large-Scale Acoustic Biodiversity Monitoring: Insights from TABMON

Event date:

Webinar with Benjamin Cretois, Norwegian Institute for Nature Research. Advancing biodiversity monitoring is crucial for meeting the EU Biodiversity Strategy targets and addressing gaps in current ecological assessments. However, collecting data to monitor the state of biodiversity is time and resource consuming. Passive Acoustic Monitoring (PAM), in combination with AI tools offers an efficient alternative to conventional data collection practices. PAM is a non-invasive method that uses sound recorders to capture wildlife vocalizations and environmental sounds over time. It is particularly valuable for monitoring elusive or nocturnal species, such as birds, amphibians, and marine mammals, that are challenging to detect visually. The "Towards a Transnational Acoustic Biodiversity Monitoring Network" (TABMON) project is an initiative to establish a transnational passive acoustic monitoring monitoring network using autonomous acoustic sensors across four different European countries: Norway, Netherlands, France and Spain. TABMON’s objective is to demonstrate how acoustic sensing, coupled with cutting-edge AI, can complement traditional monitoring methods and support the development of methods to better monitor biodiversity. In this talk, we will also share our experiences with the deployment of acoustic recorders, data management strategies, and annotation protocols. These include managing large-scale, networked deployments across diverse landscapes, designing an efficient annotation workflow, and leveraging AI tools to process and analyze massive datasets.

Leveraging AI for Climate Resilience in Africa: Challenges, Opportunities, and the Need for Collaboration

Event date:

Webinar with Amal Nammouchi, Karlstad University and AfriClimate AI. As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent’s unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for climate change adaptation and mitigation, its effectiveness and potential are contingent upon overcoming significant challenges such as data scarcity, infrastructure gaps, and limited local AI development. This talk explores the role of AI in climate change adaptation and mitigation in Africa. It advocates for a collaborative approach to build capacity, develop open-source data repositories, and create context-aware, robust AI-driven climate solutions that are culturally and contextually relevant.

Frontiers in machine learning for weather forecasting

Event date:

Webinar with Joel Oskarsson, Linköping University. Recent years have seen rapid progress in using machine learning models for weather forecasting. These models show impressive performance, matching or even outperforming existing physics-based models, while running in a fraction of the time. This is fundamentally and rapidly changing the landscape of weather forecasting today. In this talk I will discuss the factors that enabled this paradigm shift, the core machine learning methods used and the research questions at the bleeding edge of machine learning for weather. In particular I will focus on how current methods can be extended to regional and probabilistic forecasting. For regional forecasting I will showcase graph-based methods for building limited area weather forecasting models. I will also discuss how generative machine learning methods can enable probabilistic forecasting, giving much-needed estimates of uncertainty and allowing for predicting extreme weather events.

Estimation of water quality parameters using remote sensing data and machine learning models

Event date:

Webinar with Alireza Taheri Dehkordi, Lund University. The global decline in water quality, exacerbated by climate change and population growth, underscores the need for continuous and accurate monitoring of water quality parameters (WQPs). Remote sensing (RS) data, especially from multispectral satellites like Sentinel-2 and Landsat-8, offers large-scale, periodic observations for tracking WQPs. However, deriving accurate estimates solely from RS data is complex due to the intricate relationships between spectral bands and water quality indicators. This talk presents two novel machine learning approaches that leverage advanced RS data processing to enhance water quality monitoring.

Artificial Intelligence for Climate Change Mitigation

Event date:

Webinar with Alp Kucukelbir, Columbia University. Artificial intelligence (AI) has the potential to make very significant contributions to climate change mitigation. The complexity and scale of the challenge is broad. In this talk, I break down opportunities for AI to effect incremental and transformational change across multiple sectors, focusing on industries with large carbon footprints. I highlight barriers and risks to the adoption of AI, including the carbon footprint of AI usage worldwide. I focus on the multiple definitions (and ultimate importance) of "trust in AI" and its impact on the integration of AI into complex workflows. This talk is for AI practitioners looking to understand how AI fits into the bigger picture of climate change. I highlight opportunities and challenges in each sector that I hope will motivate collaboration across academia and industry.

2025 Nordic Workshop on AI for Climate Change

Event date:

The 2025 Nordic Workshop on AI for Climate Change will gather researchers from the Nordics. This one-day, in-person workshop, will take place in Gothenburg, Sweden, May 13th 2025. The workshop will feature a mix of keynotes, oral presentations, and posters around the topics of AI for climate change, including AI for biodiversity and the green transition. The workshop will be a meeting point for a wide range of researchers from (primarily) around the Nordic countries.

featured-member

Featured member, September 2025: Mohammad Kakooei

Published:

Mohammad is a researcher at the AI and Global Development Lab, specializing in applying machine learning to Earth Observation (EO). His work focuses on scalable and trustworthy AI models that use satellite data to tackle societal and environmental challenges, including poverty estimation, disaster management, and climate change impacts. He has held positions at KTH Royal Institute of Technology and Chalmers University of Technology, and collaborated internationally with partners such as Harvard University. His long-term vision is to advance AI–EO research for sustainability and the UN Sustainable Development Goals.

Featured member, August 2025: Miki Verma

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Miki is the Lead Data Scientist at Rewiring America, the leading U.S. nonprofit dedicated to the electrification of American homes. She develops data-driven tools, research, policy, and narratives to help facilitate households' transition from fossil-fuel-powered to efficient electric systems. Her recent work includes leading the development of the Residential Electrification Model, a free API that predicts bill savings, emissions reductions, and energy impacts of electrification upgrades at any U.S. address.

Featured member, June 2025: Edmond Sacre

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Edmond is a researcher at SLU Aqua (Swedish University of Agricultural Sciences) within the Unit for Protected Areas and Marine Planning. He is particularly interested in applied research questions related to mapping aquatic biodiversity and using this information to inform conservation decisions. More specifically, he is interested in how we might be able to utilize the power of machine learning and AI to map species and ecosystems, especially where data are limited.

job-openings

Postdoc in Climate Extremes & Population Health

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Uppsala University’s CLIMES centre is hiring a postdoc to study the impacts of climate extremes (heatwaves, floods, droughts) on population health, with a focus on vulnerable groups.

Open position: Associate Professor or Assistant Professor in Satellite Remote Sensing

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Chalmers University of Technology is hiring an Assistant or Associate Professor in Satellite Remote Sensing to join its leading Geoscience and Remote Sensing Division. The role focuses on advancing satellite-based Earth observation through research, teaching, and collaboration across academia and industry. Applications are open until 24 August 2025.

Open position: Assistant professor in AI for Sustainable transformation

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Lund University is hiring an Assistant Professor in AI for Sustainable Transformation at the IIIEE, focusing on cutting-edge interdisciplinary research that bridges digital technologies and climate action. The position offers strong support for external funding, teaching, and collaboration with societal partners, with a pathway to permanent employment. Applications are open until 11 August 2025, and international researchers are especially encouraged to apply.

Internship Opportunity at RISE Lund: Ecological Annotation of Drone Imagery

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RISE Research Institutes of Sweden is looking for 1–2 motivated interns in Fall 2025 to annotate drone imagery for biodiversity AI research, supporting a collaborative project with Arla. Part-time (50%), based in Lund, and perfect for students in ecology, biology, or environmental science.

news

Featured paper: Bayesian optimisation against climate change: applications and benchmarks

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The featured paper this month shows how Bayesian optimisation can support climate change mitigation by tackling optimisation problems in renewable energy and environmental monitoring. This includes a review on applications across four main use cases—material discovery, wind farm layouts, optimal renewable control, and environmental monitoring, and the proposal of benchmark problems to guide future research. The work is connected to ongoing projects at Climate AI Nordics, such as efforts to design safer solar panel materials and improve air pollution monitoring.

Norway launches six new AI research centers

Event date:

The Norwegian government has announced six new national research centers for artificial intelligence, backed by over NOK 1 billion over five years. While their main missions range from education to robotics, creativity, and decision-making, sustainability, ethics, and environmental responsibility are important themes in several. Many PhD positions on relevant topics will be announced in the coming year. The centers will be presented in a public online event on August 26.

Climate AI Nordics represented at AI+Environment Summit!

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Francesca Larosa, PhD, core team member at Climate AI Nordics, will be a session panelist at AI + Environment Summit Zurich in October! Be sure to check out this exciting event and its great lineup of speakers!

Climate AI Nordics first 9 months

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From pioneering AI models for weather and biodiversity to our first Nordic Workshop on AI for Climate Change, the Climate AI Nordics community has grown and thrived over the past 9 months. Here's a look back at our webinars, newsletters, and key milestones.

Featured paper: Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks

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This paper explores the potential for using neural networks to model atmospheric particle formation. The neural network-based model, tasked here to predict cluster binding energies, achieved the target chemical accuracy of less than 1 kcal/mol, although kernel ridge regression had a slightly lower error. Training was over an order of magnitude faster with the neural network compared to kernel ridge regression.

TreeSense Center of Excellence Officially Launched at University of Copenhagen

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The TreeSense Center of Excellence was officially launched on 24 June 2025 at the University of Copenhagen. The new research center, supported by the Danish National Research Foundation, brings together experts in remote sensing, ecology, and AI to revolutionize how we monitor global tree resources.

Climate AI Nordics to post news on job openings

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Climate AI Nordics aims to further help boost the Nordic AI-for-climate and AI-for-environment ecossystems, by now also including relevant job openings in our monthly newsletters. These job openings will also appear on our [news page](https://climateainordics.com/news/). Please get in touch with us regarding job openings that you'd like to share with our community.

Featured paper: Severe decline in large farmland trees in India over the past decade

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This study investigates changes in agroforestry tree cover across India between 2010 and 2022 using high-resolution satellite imagery (RapidEye and PlanetScope) and extensive tree label datasets. The researchers applied deep learning techniques from object counting, using heatmap-based models that transformed discrete tree labels into continuous Gaussian representations for improved detection. Their findings reveal a significant decline in large farmland trees, with up to 11% loss nationally and as high as 50% in certain hotspot areas. From 2018 to 2022 alone, over 5 million trees were lost, reflecting a rapid transformation in India's agricultural landscapes. Interviews with farmers linked this decline to shifts in cultivation practices, particularly the intensification of cropping systems.

Inaugural Nordic Workshop on AI for Climate Change sparks collaboration and looks to the future

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The inaugural Nordic Workshop on AI for Climate Change in Gothenburg successfully united leading researchers, practitioners, and innovators, fostering lively interdisciplinary discussions and idea exchange on topics ranging from biodiversity monitoring to the environmental footprint of AI. Building on this enthusiastic and collaborative atmosphere, the community is now looking forward to future events, with the 2026 workshop already confirmed to be hosted by the University of Copenhagen and the Pioneer Centre for AI. This vital initiative continues to build a strong Nordic network dedicated to maximizing AI's potential in addressing the urgent climate crisis.

Featured Project: Acoustic Monitoring of Guillemot Life at Stora Karlsö

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The AukLab project on Stora Karlsö enhances its AI-driven monitoring of Common Guillemots by adding continuous acoustic recording to its existing camera surveillance. A new system using synchronized Raspberry Pi units captures and analyzes guillemot vocalizations, integrating this audio data with visual and thermal information. Researchers aim to use machine learning on this combined dataset to gain deeper insights into social interactions, parental care, environmental responses, and potentially even detect fish arrivals acoustically. This addition represents a significant step towards creating a comprehensive digital representation of the guillemot colony for broader research access.

Climate AI Nordics now has 200+ registered people

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Climate AI Nordics recently reached a [new milestone of 150+ Nordics-affiliated researchers](https://climateainordics.com/people/), and 200+ when including outside-Nordics-affiliated reserachers. Exciting to see the network growing!

Will you join the #AIWeatherQuest?

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ECMWF invites AI/ML experts to improve sub-seasonal forecasts (days 19-25 & 26-31)—key for climate adaptation & planning. Open to all, any tools/datasets.

Climate AI Nordics Core Team

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We are proud to announce the amazing Core Team of Climate AI Nordics! Ten people spread over the Nordics will take place in the Core Team, to share the responsibility of developing the network and run our activities.

Featured Paper: Deep learning-enhanced detection of road culverts in high-resolution digital elevation models: Improving stream network accuracy in Sweden

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The new Climate AI Nordics Featured Paper is "Deep learning-enhanced detection of road culverts in high-resolution digital elevation models: Improving stream network accuracy in Sweden" by William Lidberg. In this work, Lidberg combines LiDAR data and aerial imagery with deep learning to detect 87 % of all road culverts in Sweden. These culverts are in turn used to increase the accuracy of maps featuring small watercourses.

Partnership between Climate AI Nordics and AfriClimate AI

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We are excited to have partnered with [AfriClimate AI](https://www.africlimate.ai/), a grassroots community dedicated to harnessing the power of AI for a sustainable, prosperous and climate-resilient Africa 🌍✨!

Upcoming seminars

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Climate AI Nordics will be hosting many seminars in 2025, featuring leading researchers exploring the role of AI in addressing climate change. Some speakers in the first quarter include Amal Nammouchi on leveraging large language models and deep reinforcement learning for trustworthy decision-making in energy management. Abdulhakim Abdi on the use of AI and Earth observation data to monitor ecosystems amid the biodiversity crisis. Atakan Aral on AI-driven environmental monitoring systems, and Sherrie Wang on AI applications in sustainable agriculture and climate mitigation. María J. Molina on AI's potential to predict extreme weather and inform climate strategies. Together uniting diverse perspectives on AI-driven climate solutions.

Featured Paper: ONEKANA: Modelling Thermal Inequalities in African Cities

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The new Climate AI Nordics Featured Paper is "ONEKANA: Modelling Thermal Inequalities in African Cities" by Sabine Vanhuysse and colleagues. This research addresses the pressing issue of thermal disparities in rapidly urbanizing African cities, where vulnerable populations are disproportionately affected by extreme heat due to environmental and socioeconomic factors.

Climate AI Nordics in Computer Sweden on how AI can help save the climate

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As COP29 is convening in Azerbajdzjan, Olof Mogren, co-founder of Climate AI Nordics was interviewed in Computer Sweden. "When it comes to climate change, we have to work broadly both with mitigation techniques and by adapting to the effects we are already seeing". "AI can be a tool to support these efforts".

Climate AI Nordics is now live!

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Climate AI Nordics is a network for researchers in the nordics working on problems related to tackling climate change using AI and machine learning. Our web site is now live.

newsletter

Climate AI Nordics Newsletter, September 2025

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Welcome to the September edition of the Climate AI Nordics Newsletter! This month’s issue highlights upcoming events on Earth observation, AI, and climate research, along with our featured member, Mohammad Kakooei, whose work advances scalable AI–Earth Observation for global sustainability. You’ll also find recent community highlights and new job opportunities across the Nordic region.

Climate AI Nordics Newsletter, August 2025

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The Climate AI Nordics community continues to grow, now connecting over 176 members working with AI for climate mitigation, adaptation, and monitoring. In this issue, we highlight a new survey on Bayesian optimisation for climate challenges, feature member Miki Verma from Rewiring America, and share updates from recent and upcoming events across the region. You’ll also find news on job openings, collaborations, and opportunities to get involved in shaping the Nordic AI-climate community.

Climate AI Nordics Newsletter, June 2025

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Welcome to the Climate AI Nordics Newsletter, June 2025! This month’s newsletter highlights the first nine months of Climate AI Nordics, including events, publications, and new collaborations across the Nordic region. It also features job openings, upcoming events, and a spotlight on one of our members working with AI for aquatic biodiversity mapping.

Climate AI Nordics Newsletter, May 2025

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Welcome to the Climate AI Nordics Newsletter, May 2025! Catch up on the Nordic Workshop on AI for Climate Change, major tree loss in India’s agroforestry, and upcoming events on conservation, causal inference, and digital emergency systems.

Climate AI Nordics Newsletter, April 2025

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Welcome to the Climate Change AI Nordics Newsletter, April 2025! Read about recent and coming seminars, workshops, and a monitoring the lives of Common Guillemots in the Baltic Sea using AI-based soundscape analysis!

Climate AI Nordics Newsletter, March 2025

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Welcome to the Climate Change AI Nordics Newsletter, March 2025! Read about recent and coming seminars, workshops, and a critical perspective of environmentally sustainable AI.

Climate AI Nordics Newsletter, February 2025

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Welcome to the Climate Change AI Nordics Newsletter, February 2025! Read about recent and coming seminars, workshops, and the monitoring of Baltic seabirds using AI-powered cameras and microphones.

Climate AI Nordics Newsletter, January 2025

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Welcome to the Climate Change AI Nordics Newsletter, January 2025! Read about recent and coming seminars, workshops, and the modelling of road culverts in high-resolution digital elevation models.

December 2024

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Welcome to the Climate Change AI Nordics Newsletter, December 2024! Read about recent and coming seminars, workshops, and the modelling of thermal inequalities in African cities.

November 2024

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Welcome to the Climate Change AI Nordics Newsletter, November 2024! Read about recent and coming seminars, workshops, and publications from the network's researchers.