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/Postdocs and more senior scientists within the MicroAM project. Responsibilities and qualifications If you are you interested in numerical simulations of advanced manufacturing processes which involve
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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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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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of an ambitious and team-based research environment with around 50 international staff members, postdocs, and PhD students. Responsibilities and qualifications The successful candidates will contribute
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characterize antenna- and metasurface-based solutions for both uniform as well as focused field distributions throughout dedicated and realistic examples of lossy media such as human tissue and fluid-cell
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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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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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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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spatial distribution of coupled cavities for photonic reservoir computing Validate the analysis on a small scale-circuit (< 10 coupled cavities) The doctoral candidate (DC) is expected to travel to network