866 data-"https:" "https:" "https:" "https:" "https:" "https:" "P" "LaTIM Brest" positions in Denmark
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, which is a collaboration between the Department of Business Development and Technology and the Department of Digital Design and Information Studies. The project ‘Practice Resonant AI Ethics for the Public
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. For further information, please contact Professor, dr. scient. et techn. Bo Brummerstedt Iversen (bo@chem.au.dk). Applications including CV, full publication list, references and description of qualifications
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information For further information, please contact: Professor Torben Heick Jensen, thj@mbg.au.dk, phone +45 60202705 Application procedure Shortlisting is used. This means that after the deadline
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information For further information, please contact: Professor Torben Heick Jensen, thj@mbg.au.dk, phone +45 60202705 Application procedure Shortlisting is used. This means that after the deadline
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, timing, power, and sign-off) Hardware accelerator development for deep learning, edge AI, and data-intensive workloads Energy-efficient and high-performance accelerator design Hardware–software co-design
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information For further information, please contact: Assistant Professor Emil Laust Kristoffersen, +45 29271306, emillk@inano.au.dk . Application procedure Shortlisting is used. This means that after
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main areas of work: Exploration of heterogeneity in GDM risk and GDM subtypes and application of these insights to develop a GDM risk prediction model, based on data from The Danish Blood Donor Study
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administrative staff, and students. You should also be structured and meticulous in your work, which is essential when collecting and managing research data. Qualifications You are expected to hold a MSc or PhD
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with teamwork in a great team Additional information Your place of work will be the AU International Centre at Høegh Guldbergs Gade 4a in Aarhus. The position is paid according to state agreements. Who
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum