Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
and across disciplines within the Pioneer Center Land-CRAFT Contribute to development of teaching and PhD supervision activities in the Dept. of Agroecology or other departments at AU with which we
-
qualification, you must hold a PhD degree in computer science, software engineering, biomedical engineering, data science, or a similar field. Your project management skills include: Experience in technical
-
, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning, and
-
on educating engineering students at all levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 300 dedicated employees. Read more about us at www.energy.dtu.dk. Technology for people
-
qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning
-
and analysis, human-machine interaction, productivity monitoring, and proactive personalized feedback and learning methods (using augmented and/or virtual realities). We seek excellent candidates with
-
visit our homepage . Expectations and qualifications Applicants must hold an MSc, DVM, or MD degree, and a PhD, with research experience within one or more of the following areas: Clinical or molecular
-
relevant (e.g., teach and co-supervise PhD and MSc student projects). Dissemination of your research through publications in “top rank journals of the field ” and attendance at conferences. Qualification
-
forecasting. You will get the opportunity to participate and influence the development of advanced forecast solutions combining weather forecasts and novel machine learning/statistical forecasting methods
-
thermomechanical process simulations such as casting and welding. The research activities at SDU-ME spans widely from fluid mechanics, condition monitoring, machine learning, fatigue, maritime structures