Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
microtumor models. This work addresses a critical knowledge gap in cancer immunobiology and supports the development of more accurate disease models. Duties The main duty for a doctoral student is to devote
-
The research in Theme A provides opportunities to address issues on measuring, assessing, and modelling of Quality of User experience (QUX). This includes personalizing QUX in novel intelligent realities
-
) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
-
be paid to the following experiences: -Experience in sampling and analyses of building materials -Experience in Life Cycle Analysis in construction sector. -Experience in building information modeling
-
. Programming gene circuits Modeling and designing synthetic DNA components Construction of Chemical Reaction Networks (CRNs) Simulation and analysis using MATLAB and Visual DSD Robust analysis of various modules
-
from satellite, aircraft or ground sensors to understand, model and retrieve parameters relevant to the processes driving our atmosphere, biosphere, hydrosphere and cryosphere. About the research project
-
methods in applied mathematics and computational modeling, this specific project aims to uncover new insights into how blood cells form in both healthy and disease states. A key objective is to model
-
or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health
-
rate, and virtually nothing is known about a putative connection between these mutation rates. Using several Drosophila melanogaster model systems, in combination with quantitative genetics, experimental
-
methods, including modern machine learning methods, to draw inferences from register data. A third project “Integrative machine and deep learning models for predictive analysis in complex disease areas“ is