Sort by
Refine Your Search
-
projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
-
or Computer Science; Human-computer Interaction, Spatial Cognition or related areas; Engineering, Applied Mathematics, Statistics or another Quantitatively Oriented Discipline. Application procedure Your complete
-
research questions, and be able to think critically and develop your own scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data
-
, statistics, data science, and public health. The goal is to develop new methods that allow researchers to learn from sensitive health data without compromising individual privacy. Using unique, nationwide
-
, mathematics, biology, and epidemiology, developing and applying novel statistical methods and deep learning approaches for global health challenges. The group’s research spans disease modelling, genomic
-
PhD position in environmental toxicology and endocrine disruption: Focus on new endpoints in zebr...
organism Proficiency in laboratory techniques, including OECD fish test protocols, histopathology analysis, and behavior analysis. It is an advantage if the candidate has experience in statistical analysis
-
statistical models to understand the ecological and phylogenetic factors playing a role in the evolution of the host-microbiome systems. The project will use the vast number of paired host and microbiome
-
statistical modeling with clinical insight, aiming to improve risk prediction and inform sex-specific prevention strategies in atrial fibrillation patients. The research will be conducted in close collaboration
-
for efficiency monitoring and fault detection, combining sensor data, system layout knowledge, and physical principles to extract spatial-temporal features and predict equipment behaviour; 2) Statistical anomaly
-
, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph representation learning. Programming skills