154 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" positions at Technical University of Denmark
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
, or a related field. Have documented experience in some of the following: Computational materials modelling or quantum mechanical simulations (e.g. DFT, MD). Machine learning / deep learning (preferably
-
about DTU Aqua at https://www.aqua.dtu.dk/ ; and the section for Fish and Shellfish diseases here: https://www.aqua.dtu.dk/english/research/fish-and-shellfish-diseases If you are applying from abroad, you
-
). You can read more about DTU Sustain at https://sustain.dtu.dk/en/ If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark
-
research for the tenure track period CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience
-
highlights, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here
-
deliverables with high standards of accuracy and clarity. Teach and supervise BSc and MSc student projects, and be co-supervisor for PhD students We are looking for candidates with: Skills in molecular biology
-
science. Teach relevant courses, supervise BSc, MSc and PhD students, and mentor postdocs and tenure track assistant Professors. Contribute to the strategic development of the department’s research and
-
within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer
-
for teaching and research CV including employment history, list of publications indicating scientific highlights, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation
-
of strains) to in-field testing of up to 800 strains. The scale and standardized approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modelling