Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
computational teams Access to unpublished validation data unavailable to existing agents State-of-the-art computing capable of running dozens of local large language models concurrently Data from emerging
-
data-driven use cases across VIB. Working directly with data, pipelines, transformations, models and code, you build and implement robust and scalable data solutions as part of your day-to-day work. The
-
mathematical models to address fundamental questions in biology. Examples of research topics include but are not limited to: development of new AI architectures for biology and hybrid models that combine deep
-
About us The Laboratory of Causal Systems Immunology combines large-scale in vivo perturbation experiments, advanced mouse models of immune diseases and causal learning to uncover how genes shape
-
human and animal models. Doing a PhD at our Center Embedded within both VIB and KU Leuven , we offer a unique PhD experience with access to state-of-the-art facilities, expertise, and training. PhD
-
Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
interested in recruiting faculty members who use and develop artificial intelligence methods and mechanistic mathematical models to address fundamental questions in biology. Examples of research topics include
-
. Ideally, the candidate will develop and/or make use of innovative reductionist (i.e. 3D tumoroid models) or in vivo models (i.e. mouse genetics, in vivo lineage tracing, PDXs,…) and establish clinical
-
, the candidate will develop innovative reductionist (i.e. 3D tumoroid models) approaches and unique in vivo models (i.e. mouse genetics, in vivo lineage tracing, PDXs,…) to gain novel mechanistic insights and
-
About the lab The Laboratory of Causal Systems Immunology combines large-scale in vivo perturbation experiments, advanced mouse models of immune diseases and causal learning to uncover how genes
-
. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and