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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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of electrolyzer technologies, digital twins, model order reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration
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BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and
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Design invites applications for a PhD stipend in the field of secure machine learning within the general study programme Electronic and Electrical Engineering; as per November 1, 2026, or as soon as
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epidemiology, and working with methods like random forest and targeted learning, the candidate will contribute to the development of interpretable survival models and doubly robust estimation methods
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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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technologies Development and utilization of high throughput methods for characterizing and quantifying the physicochemical behavior of food macromolecules in complex matrices. Modelling and the use of machine
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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine