146 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Technical University of Denmark
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Professor Martijn Wubs (mwubs@dtu.dk ), Dr. Jake Iles-Smith (jake.iles-smith@sheffield.ac.uk ) You can read more about the Department of Electrical and Photonics Engineering at https://electro.dtu.dk
-
information Further information may be obtained from Professor Athanasios Kolios, atko@dtu.dk You can read more about DTU Wind at https://wind.dtu.dk/ If you are applying from abroad, you may find useful
-
more about career paths at DTU here . Further information May be obtained from Professor Lone Gram, gram@bio.dtu.dk You can read more about DTU Bioengineering at https://www.bioengineering.dtu.dk
-
position, please contact Professor Jakob E. Bardram at jakba@dtu.dk . Read more on CARP at https://carp.dk/ and about DTU Health Tech at www.healthtech.dtu.dk . Applications received after the deadline
-
in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field Has strong theoretical and practical experience in deep learning Has hands
-
Job Description The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we are seeking a highly talented and motivated PhD student
-
digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
-
. To do so, you will combine atomistic simulations (density functional theory and ab-initio molecular dynamics simulations) with new machine learning models to parameterize machine learning force fields
-
in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field Has strong theoretical and practical experience in deep learning Has hands
-
decision-making; and (iii) decision-support to compare trade-offs and policy alternatives. The position is supervised by Professor Francisco Pereira, with co-supervision from colleagues in machine learning