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Field
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our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Associate
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University. The PhD candidate will be working with a team of distinguished researchers: Professor Charmine Hartel (Department of Management, Monash Business School; Director, Opportunity Tech Lab) Associate
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databases such as the Web of Science. A few start-up interviews (max. 15) with people from evaluation committees, employees from The Danish Innovation Fund as well as prior applicants may also be conducted in
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years in the Netherlands. Do you want more information? For more information about the position, please contact Dr. Paul Smeets, Associate Professor, via email: paul.smeets@wur.nl or phone: +31 317
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general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Professor Jesper Hattel and Associate Professor Chris
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degree in Computer Science, Math, Statistics or Engineering programs. Ph.D would be an asset. Knowledge of classification algorithms, vision-language models (VLM) and Hugging Face Transformers is an asset
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or very good university degree (diploma, master's degree) in transport or related study programs (e.g., civil engineering, industrial engineering, computer science, etc) with a solid foundation in transport
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Professor Dr. Klaudia Carcani, Department of Computer Science & Communication, Klaudia.carcani@hiof.no Dean, Susanne Koch Stigberg, Faculty of Computer Science, Engineering and Economics, susanne.k.stigberg
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information? For more information about the position, please contact Andries Richter, Associate Professor, at +31317484738 or via email andries.richter@wur.nl . For more information about the procedure, please
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning