52 associate-professor-computer PhD positions at Technical University of Denmark in Denmark
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period of employment is 3 years. You can read more about career paths at DTU here . Further information Further information may be obtained from Professor Stefan Røpke (ropke@dtu.dk ) or Associate
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general planning of the PhD study programme, please see DTU's rules for the PhD education . We offer DTU is a leading technical university globally recognized for the excellence of its research, education
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. The project will be supervised by Associate Professor Martin Hansen (DTU Sustain), and co-supervised by Professor Hans Peter Arp (NTNU Chemistry), Senior Scientist Anna Rosenmai (DTU Food), Veerle Jaspers (NTNU
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co-supervised by Associate Professor Qianwen Xu at KTH, with the opportunity to undertake a research stay of 3-6 months in Stockholm, Sweden. Responsibilities and qualifications Power electronic
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. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . We offer DTU is a leading technical university globally
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requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . We offer DTU is a leading technical university globally recognized for the excellence of its
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agreed upon with the relevant union. The period of employment is 3 years. You can read more about career paths at DTU here . Further information Further information may be obtained from Associate Professor
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of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . We offer DTU is a
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designing a sequential array of cell-based in vitro assays and associated biomarker measures Performing a comparative study with the sequential array of cell-based models and an in vivo animal study Design
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon