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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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research in various areas of mobile network systems, multimedia and AR/VR/XR systems, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts
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to knowledge in one or more of the following areas: machine learning, cybersecurity, or computer systems Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in
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interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#. You can present
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knowledge of German or the willingness to acquire such knowledge is essential. Work Location(s) Number of offers available1Company/InstituteDepartment of Computer ScienceCountryAustriaCityInnsbruckPostal
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Science, or Information Science, with no more than five years post receipt of the PhD. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data
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migraine and other headache disorders, neuroepidemiology, and other neurodegenerative disorders. We use advanced biostatistical modeling, artificial intelligence (AI), machine learning, and neuroinformatics
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) documented experience in research work on machine learning models with applications in life-science domain; 4) ability to conduct independent research as well as to work effectively in a team, including
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, clinical data and AI-driven modelling for cancer research! In this role, you will bridge the gap between machine learning, computational biology, and haematological oncology. You do not need to arrive as an
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interpretability, explainable AI, causal representation learning, model probing, attribution methods or faithfulness evaluation. Strong programming skills in Python and experience with modern machine learning