Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
and other 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
-
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
-
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
-
managers and center leadership on practical people questions and possible next steps. Help leaders prepare for difficult conversations and address team issues proactively. Advise leaders and actively
-
of both biology and (agent-driven) software engineering Fluency in written and spoken English Embraces complexity Our offer An interdisciplinary environment spanning wet-lab, technology-development and
-
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
-
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
-
from large‑scale immune repertoire data and in translating these insights into rational, model‑driven prioritization of high‑quality nanobody candidates. Working at the interface of immunology, protein
-
from large‑scale immune repertoire data and in translating these insights into rational, model‑driven prioritization of high‑quality nanobody candidates. Working at the interface of immunology, protein