Christian Igel

The Department of Computer Science at the University of Copenhagen is inviting applications for several postdoctoral fellowships in the areas of Machine Learning, Quantum Computing, and Responsible Artificial Intelligence. These positions are part of large, interdisciplinary projects, including the AI4NaturalFood initiative, which aims to revolutionize sustainable plant-based food production by integrating advanced machine learning methods with food science. The projects are funded by the Novo Nordisk Foundation, with a significant grant supporting research and collaboration across departments and international partners. Research Areas: Machine Learning for Sustainable Food Processing: Focus on multi-modal representation learning to design sustainable food processing methods. The project involves developing ML techniques to model the complex properties of plant-based ingredients, integrating data from various sources (e.g., time-series, microscopy, rheology), and applying active learning to guide experimental data generation. Collaboration with the Department of Food Science is central, and the postdoc will bridge computer science and food science teams. Quantum and Tensor Network Algorithms: Positions are available in quantum algorithms, tensor network methods, and quantum error correction. The research group collaborates closely with the Niels Bohr Institute and the Novo Nordisk Center for Quantum Computing, focusing on theoretical and computational advances in quantum information science. Responsible Machine Learning: Opportunities exist for postdocs or assistant professors to conduct research on responsible AI, including fairness, transparency, and accountability in machine learning. The position is within the Foundations of Responsible Machine Learning group and the broader DeLTA lab, with expectations for high-impact publications and research-based teaching. Supervision and Collaboration: The projects are supervised by leading academics: Professors Christian Igel, Remko Boom, Raghavendra Selvan, Michael Kastoryano, and Amartya Sanyal. The environment is highly collaborative, with opportunities to co-supervise PhD students, participate in multidisciplinary teams, and engage with international research partners. Funding and Support: These are fully funded positions, with a 24-month contract and potential for extension. The Novo Nordisk Foundation grant ensures robust support for research activities, including salary and project expenses. The University of Copenhagen offers a dynamic research environment and access to state-of-the-art facilities. Eligibility: Applicants must have a PhD in a relevant field (machine learning, computer science, mathematics, physics, or related disciplines). A strong publication record in top-tier venues is required. Experience in food technology is a plus for the ML/food science project. Excellent English skills are essential; Danish is not required. Candidates should be motivated, collaborative, and eager to work in interdisciplinary teams. Application Process: Applications must be submitted electronically, including a motivation letter, CV, diplomas, and transcripts. Applicants must indicate their preferred principal supervisor. The deadline for applications is January 15, 2026. For more information, see the official call and project links. Keywords: machine learning, representation learning, food science, quantum computing, tensor networks, responsible AI, sustainable food processing, active learning, artificial intelligence.

The University of Copenhagen invites applications for a fully funded PhD fellowship in Machine Learning for Environmental Sciences, based in the Department of Computer Science, Machine Learning Section. This position is part of the Global Wetland Center (GWC), funded by the Novo Nordisk Foundation, and focuses on developing machine learning methods to model greenhouse gas fluxes using multimodal remote sensing and ground-level data. The research aims to advance wetland-based climate change mitigation strategies through biogeochemical and hydrological modelling, satellite remote sensing, and artificial intelligence. The PhD student will work on hybrid modelling approaches, combining process-based models and deep learning, as well as self-supervised learning, to address challenges of limited reference data. The project involves creating new global-scale datasets and contributing to high-impact research targeting top-tier computer science and remote sensing venues. The student will collaborate with researchers at the Global Wetland Center, DHI A/S, GEUS, and be affiliated with the Danish Pioneer Center for AI. There is also an option to join the ELLIS PhD program. Applicants should have a degree equivalent to a Danish master’s in computer science, applied mathematics, geomatics, or related fields, with a strong background in machine learning and computer vision. Experience with remote sensing modalities and proficiency in Python (especially PyTorch, GDAL, Rasterio, GeoPandas) are required. Knowledge of differentiable programming is a plus. Good English skills are essential. The position offers a monthly salary starting at 31,242 DKK (approx. 4,180 EUR) plus pension, and includes benefits such as paid vacation, parental leave, and public healthcare. The application deadline is April 6, 2026. To apply, submit your application electronically via the provided portal, including a motivated letter, CV, diplomas, transcripts, publication list, and contact details of three referees. Clearly mention the project and the principal supervisor, Prof. Christian Igel, in your application. For more information, contact Prof. Christian Igel ([email protected]) or Assistant Prof. Nico Lang ([email protected]). This opportunity is ideal for creative students passionate about interdisciplinary research at the intersection of machine learning, environmental science, and remote sensing, aiming to make a global impact on climate change mitigation.

Open PhD fellowship in Machine Learning for Environmental Sciences at the Department of Computer Science, University of Copenhagen , within the Global Wetland Center . This opportunity is aimed at candidates interested in combining computer science , machine learning , and environmental sciences , with a likely focus on data-driven methods for wetland or ecological research. The post also notes an option to join the ELLIS PhD program (European Laboratory for Learning and Intelligent Systems), which may be relevant for students seeking a broader AI/ML research network. The announcer is Christian Igel , Professor at the University of Copenhagen. No email address, stipend amount, tuition coverage, or other funding specifics are stated in the post excerpt. Deadline: August 2, 2026. How to apply: Use the external application link provided in the call and submit the application before the deadline.