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be an advantage: Quantitative behavioral analysis. Motor or sensorimotor neuroscience. Computational analysis of neural population activity. Machine-learning approaches for neural or behavioral data
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personal language learning assistants (1 year) and AI security (1 year). Furthermore, there will be the possibility to gain experience as a co-supervisor for PhD students associated with these projects, and
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with its grounding in cognitive theory. The position suits candidates with a PhD in Human-Computer Interaction or a related field who enjoy making empirical, methodological, and technical contributions
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to think independently, in human-AI interaction. You will connect the project's technical work with its grounding in cognitive theory. The position suits candidates with a PhD in Human-Computer Interaction
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. An interdisciplinary interdisciplinary environment, in which the candidate will be able to exchange across research fields (machine learning, bioinformatics, biology, and quantitative genetics) and applications (plant
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Postdoc in Digital Education and Empowerment for Patients with Atrial Fibrillation and Comorbidities
research funding Language skills in Danish and English (oral and written) Access to a car Qualification requirements Appointment as postdoc requires academic qualifications at PhD level. How to apply Your
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at the interface between mechanics and machine learning? We are looking for a postdoctoral researcher for a fixed term, full time position at the Department of Materials and Production on Aalborg campus, starting 1
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the development and demonstration of a “fast decision machine” that can provide a qualified basis for early decisions concerning future aseptic production. You will plan and carry out your research systematically
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for quantifying methane emissions. A special focus will be on exploring how our local drone data can be combined with satellite observations, using machine learning and other modelling approaches, to improve
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how