10 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Helmholtz Zentrum Hereon" research jobs at VIB in Belgium
Sort by
Refine Your Search
-
biology, and translational disease research, and offers strong opportunities for methodological development, interdisciplinary collaboration, and high-impact publications. Your profile We are looking
-
Chart tumour evolution using cutting-edge long-read and single-cell multiomics Who we are The Integrative Cancer Genomics lab (Jonas Demeulemeester) is embedded within the VIB – KU Leuven Center
-
) the development of translational strategies in circuit neuroscience and (4) the development of computational models and methods for neuroscience. The goal is to implement advanced brain-on-chip models and CRISPR
-
diverse classes of inhibitory neurons are specified and integrated into brain circuits during development. Our work bridges developmental neurobiology, disease modeling, and systems neuroscience. To do
-
work with a team of scientists interested in (1) mechanisms of neurodegeneration, (2) cellular determinants of disease, (3) functional genomics, (3) the development of translational strategies in circuit
-
for the technology, including brainstorming and literature review. Coordinate with biopharma partners to streamline development and promote industry adoption. Participate in grant writing to secure project funding
-
centrosome is essential for early embryonic cell divisions, we still do not understand the profound “centrosome reduction” it undergoes during spermatogenesis and how this pathway impacts embryo development
-
New Year Professional development through regular mentorship, training opportunities, and collaborative projects. The planned starting date is 1 January 2026 How to apply? Submit your
-
environment to help you continue pursuing your scientific career. More info and application: https://raeslab.sites.vib.be/e... We offer VIB-KULeuven is an exciting, interdisciplinary research institute with
-
. Responsibilities Model Development: Build and train advanced deep learning architectures (e.g., CNNs, Transformers, Generative Models) to decode the regulatory logic of genomic enhancers in GBM