Sort by
Refine Your Search
-
year project, funded by the DDLS program, we aim to develop AI-based tools in design of affinity ligands, such as the prediction of binding interactions between proteins. Data-driven life science (DDLS
-
tools for end-to-end processing of next-generation sequencing data, from raw data to variant discovery (e.g., GATK pipeline). Experience with programming languages (e.g. bash, Python, and R). Experience
-
clinical genetics, clinical immunology, pathology, neuro biology, neuro-oncology, vascular biology, radiation science and molecular tools. Department activities are also integrated with the units
-
at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
-
Are you interested in developing computational tools and learning strategies for understanding health and disease at the microscopic scale? Would you like to be part of a research team with skilled
-
different conditions using existing software (written in Fortran). Analysis of data using quantitative genetics tools (e.g., calculation and comparison of genetic and phenotypic covariance matrices
-
. The project will develop fundamental theory and tools that will be key for understanding biological mechanisms causing diseases that are due to gene dysregulation, such as cancer. The core of the project is a
-
. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel
-
of small cryptic plasmids in the development and spread of antibiotic resistance, and ii) Use machine learning tools to examine the complex interplay between bacterial hosts, various plasmids and resistance
-
of data-driven precision tools, seeking to improve on current paradigms for clinical risk predictions and individualized diagnostic paradigms. Improving atherosclerotic risk prediction has been a primary