Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- SciLifeLab
- Uppsala University
- Monash University
- University of Bergen
- ;
- Karolinska Institutet
- Newcastle University
- Technical University of Denmark
- Uppsala universitet
- ; Newcastle University
- ; Swansea University
- ; UCL
- ; University of Warwick
- AcademicTransfer
- Amsterdam UMC
- Conway Institute of Biomolecular & Biomedical Research
- ETH Zurich
- Erasmus MC (University Medical Center Rotterdam)
- KU Leuven
- Karolinska Institutet, doctoral positions
- Swansea University
- Synklino
- UNIVERSITY OF EAST LONDON
- University Paris Cité
- University of Copenhagen
- University of East London
- Vrije Universiteit Amsterdam (VU)
- 17 more »
- « less
-
Field
-
points for drug-discovery. However, methods to interrogate and manipulate PPIs are not well established. Moreover, intrinsically disordered regions – segments of protein with no fixed structure
-
, equipment, and travel related to the project. Overview Machine learning aims to transform the drug discovery landscape through the prediction of potential new therapeutics with unprecedented speed and
-
PhD Studentship: Integrating computational physics-based simulation and machine learning with drug discovery pipelines Award Summary 100% home fees covered, and a minimum tax-free annual living
-
PhD Studentship: Cyclic peptide discovery for intracellular protein targets Award Summary 100% home fees covered, and a minimum tax-free annual living allowance of £19,237 (2024/25 UKRI rate), plus
-
, drug discovery and development for rare diseases. Our innovative and ambitious joint-PhD program will train highly qualified young scientists in new scientific and technological knowledge. At the end
-
critical challenges in the field of organoid technologies for disease modelling, drug discovery and development for rare diseases. Our innovative and ambitious joint-PhD program will train highly qualified
-
of the PhD project is to develop computational approaches to identify small molecule and peptide ligands that target therapeutically relevant receptors, with the vision to accelerate the drug discovery process
-
the drug discovery process. The project will be focused on addressing several challenges in structure-based drug design using artificial intelligence. Recently developed techniques for the prediction
-
of the druggable genome can be increased substantially. A major obstacle in RNA-targeting drug discovery is the lack of knowledge on how to obtain drug-like RNA ligands. The main goal of this project is therefore
-
integration, analysis, visualization, and data interpretation for patient stratification, discovery of biomarkers for disease risks, diagnostics, drug response assessment and monitoring of health. The precision