Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
advanced post-training techniques that align pre-trained transformer-based LLMs with cultural values and linguistic conventions. By focusing on low-resource languages, this project addresses challenges
-
operators, transformers/LLM) and NN training. Strong Python programming skills (as a plus: C++ or Julia) and knowledge of scientific computing libraries (numpy, scipy, JAX…) and machine learning libraries
-
with machine learning, specifically deep learning (Transformers, RNNs, CNNs, etc.), and XAI techniques. Experience with collaborative, cross-cultural research environments. Who we are At the Department
-
with machine learning, specifically deep learning (Transformers, RNNs, CNNs, etc.), and XAI techniques. Experience with collaborative, cross-cultural research environments. Who we are At the Department
-
. Experience in working with IO-archives and proficiency in reading and writing in English and French is a requirement. A proven ability to work with social historical, cultural historical, prosopographical and
-
, electrical engineering, communication engineering, computer science, or a related field. Documented experience with deep learning techniques (e.g., CNNs, Transformers) Strong programming skills in Python and
-
, skills, and achievements that demonstrate the ability or potential to produce high-quality research in relation to the CLAPS project and publish it in international journals. This may entail capacity
-
resource recovery pipelines You will contribute to the translation of structural and biophysical insights into technologies or methods for transforming recovered biopolymers into valuable products or process
-
. The ability to work critically with archaeological and ancient historical sources. A methodological and structured approach to work. Good organisation skills and excellent attention to detail. Flexibility and
-
. However, powerful as it is, MagTense is at present limited in its capability to model complicated magnetic systems for two reasons. First, all magnetic sources in a simulation interact, leading to