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The Department of Computer Science at the University of Copenhagen is offering five PhD positions in the Machine Learning Section, focusing on cutting-edge topics such as Green Computation Scheduling and Resource Efficiency for Generative AI. The Green Computation Scheduling project aims to reduce carbon emissions from computation by designing algorithms that schedule tasks during periods of low carbon intensity, leveraging advances in online and reinforcement learning. Applicants for this project should have a strong theoretical background and mathematical skills, with experience in online learning, bandits, and reinforcement learning considered an advantage. The project is supervised by Yevgeny Seldin and Raghavendra Selvan. The Resource Efficiency for Generative AI project investigates methods to make Large Language Models more resource-efficient, exploring novel algorithms, training paradigms, and hardware optimization to improve sustainability, safety, fairness, and access. Candidates for this project must possess strong skills in mathematics and machine learning and be motivated to contribute to the UN sustainable development goals. Supervisors for this project include Christina Lioma, Maria Maistro, and Raghavendra Selvan. The application deadline is September 30, 2025, and applications should be submitted via the official university portal. For further details or inquiries, candidates are encouraged to contact the relevant supervisors via the provided email addresses. While funding details are not explicitly stated, these are PhD fellowship positions, which typically include full funding and tuition coverage at the University of Copenhagen.

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.