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Job Description The aim of this PhD project is to investigate the immediate and long-term deformation behaviour of plain and fiber-reinforced concrete using distributed fibre-optical sensing (FOS
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will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be used. This part of the three
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale
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developing 3D vision algorithms for object detection, recognition, and scene understanding to support planning and task execution in dynamic environments. Publishing research findings in leading international
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hardware modification. The AI will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be
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, Responsibilities and qualifications Electricity markets are undergoing a rapid transformation: Market participants are deploying AI algorithms towards making their bidding decisions. AI algorithms are instructed
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content may be obtained from Head of Section for E-Mobility and Prosumer Integration, Senior Researcher Peter Bach Andersen (petb@dtu.dk ), Assistant Professor Jan Engelhardt (janen@dtu.dk ), and Postdoc
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well organized, structured, self-driven, and enjoy interacting and collaborating with colleagues, including PhD students and postdocs. You are also expected to take part in the supervision of BSc and MSc
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evaluate BCI algorithms for decoding motor intentions Integrate BCI systems with KAIST’s advanced exoskeletons Conduct experiments with healthy subjects and stroke patients Collaborate closely with a KAIST
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data Design algorithms for correlating low-level events into process-level attack models Contribute to joint framework development with TU/e on continual learning Collaborate with industry partners