Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
Computer-Aided Drug Discovery modeller to join our team. Your new role Your main responsibilities will be to drive Drug Discovery projects by identifying, developing, and delivering high quality modelling
-
, to various qualitative methods; also emphasizing the interplay between qualitative and quantitative methods and data. There is a growing focus on novel computational methods such as NLP, machine learning, and
-
, dplyr etc.,) and version control system (git, bitbucket etc.). Experience in use of AI and Machine Learning techniques using Python (sklearn, statsmodels, pandas, numpy) is desirable. Solid and proven
-
are expected to contribute to our students’ educational development. For this reason, you are expected to teach and supervise students at all levels, including supervision of PhD students. Our study programs
-
. The candidates will be responsible for collaboratively building up the area of supply chain digitalisation with a primary focus on data governance, artificial intelligence, and applied machine learning. Successful
-
intelligence and machine learning for data analysis, scenario design, optimisation, etc.). Candidates are expected to teach and supervise students in our study programmes, particularly the MSc in Engineering
-
will lead efforts to apply state-of-the-art AI techniques (machine learning, deep learning, generative models, etc.) to the discovery and development of new materials in critical domains: water, energy
-
expected to be fluent in at least one of these languages, and in time are expected to master both to be able to teach BSc. students. As formal qualification you must hold a PhD degree (or equivalent). You
-
working with 3D Image analysis in collaboration with users from many different fields. A central part of the team’s activities lies in creating new tools using machine learning enabling better and faster
-
value theory, non- and semiparametric statistics, missing data problems, causal inference, graphical models, event history analysis, benchmarking, spatio-temporal modelling, machine learning, and