Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
, a large initiative funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site
-
, the following qualifications will be advantageous: Published peer-reviewed articles in international journals. Training in statistics and machine learning. Experience with generative AI modeling Bioinformatics
-
, 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
-
of machine-learning algorithms for unmanned aerial vehicles; dissemination of the results in international conferences and journals; proposal writing for external funds. Your profile The successful candidate
-
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
-
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
-
to demonstrate the following qualifications: Computational social science/machine-learning techniques Large-scale data collection Large-scale text analysis Large-scale visual analysis Professional interest in and
-
Qualifications: PhD in computer science, mathematics, statistics, or related fields (by the start date). Strong background in stochastic optimization, machine learning, or mathematical statistics. Track record
-
Natural Language Processing, Machine Learning, or a similar area. Expertise in large language model architectures and training paradigms (transformer models, fine-tuning strategies, RLHF, etc.). Interest in
-
patterns, and extreme climate events remains a subject of debate. Using a combination of climate modelling, statistical methods, and machine learning, ArcticPush aims to uncover the conditions under which