236 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Denmark
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, Aalborg University is known worldwide for its high academic quality and societal impact. The Department of Electronic Systems employs more than 200 people, of which about 90 are PhD students, and about 40
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, to better understand the complex interplay of the many factors that drive cardiometabolic disease. You can learn more in the Executive Summary of CBMR's Strategy 2024–2028 . CBMR was established in 2010
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thesis project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer
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stronginterest and experience with GIS data and tools for urban mobility with someprogrammingskills of Python/R, JavaScript, database management environments, Geographical AI and machine learning workflows
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experience with Mathworks and Aspen+ products is required for the position Qualification requirements Appointment as postdoc requires academic qualifications at PhD level. Who we are AAU Energy is a dynamic
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: Application, stating reasons for applying, qualifications in relation to the position, and intentions and visions for the position Curriculum Vitae (CV) Diplomas (master's degree diploma and PhD diploma) List
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expected to take a leading role in the research program, supported by a Villum Investigator Grant, in which femtosecond and picosecond laser pulses are used to study molecules and molecular complexes
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project. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power
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results at conferences, Teach and supervise BSc and MSc student projects, and be co-supervisor for PhD students You must have a PhD in macro-energy system modelling or a similar field. Strong coding skills
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling