Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
in unprecedented ways. She/he will play a central role in directing and carrying out the above program, drawing on the skills present in the two partner groups. More generally, she/he will also benefit
-
Excellence Programme , which prioritizes the recruitment of female scholars to professorial roles. For an initial period of six months, the University will consider applications exclusively from female
-
independent research group in Immunology. The successful candidate will be expected to develop a bold, innovative, and high-impact research program that complements and enhances INEM’s scientific strengths
-
, Argument Mining, Abusive language detection, Counter-argument generation Skills and profile: • Master degree in Artificial Intelligence, Data Science, Computer Science or Computational Linguistics is
-
About the FSTM The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Faculty of Science, Technology and Medicine
-
various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
-
are particularly interested in candidates who combine computational biology, data science, and machine learning/AI with deep biological insight. While wet lab activities are welcome, they are not mandatory. However
-
, cancer, immunology and virology (see www.igmm.cnrs.fr ). We are enthusiastic about welcoming new group leader colleagues who share our core values of collegiality, interdisciplinarity and scientific
-
Excellence Programme , which prioritizes the recruitment of female scholars to professorial roles. For an initial period of six months, the University will consider applications exclusively from female
-
, https://hal.science/hal-04930868 . [2] Peyré, G., Cuturi, M., et al. (2019). Computational optimal transport: With applications to data science. Foundations and Trends in Machine Learning, 11(5-6):355–607