Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- Chalmers tekniska högskola
- KTH Royal Institute of Technology
- Lunds universitet
- SciLifeLab
- chalmers tekniska högskola
- Karolinska Institutet
- Karolinska Institutet (KI)
- Linköping university
- Linköpings University
- Sveriges Lantrbruksuniversitet
- Umeå University
- University of Lund
- Uppsala universitet
- 4 more »
- « less
-
Field
-
methods to rigorously assess the safety and effectiveness of medications in real-world patient populations. Defining individualized treatment strategies: Leveraging traditional and causal machine learning
-
-renal diseases. Our research spans large-scale register and laboratory data, causal and predictive modeling, and computational image analysis of kidney biopsies. About the Research Group: Led by Professor
-
learning and its application within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning
-
using genetic data from family-based studies as well as -omics data for integrative deconvolution and machine learning methods for prognosis and therapeutic biomarker development. The collaborative
-
, obtained within the last three years prior to the application deadline Strong background in machine learning, statistical modeling, and big-data analytics. Experience with infrastructure or transportation
-
description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
-
The postdoctoral researcher will work with computer-based analytical methods and large databases to develop theory and methodology for utilising aggregated data from archaeology, genetics, and linguistics, thereby
-
environment for all employees through mutual respect and tolerance. Description of the project The postdoc will leverage existing high-throughput data from large scale cohorts and large family cohorts
-
you can say yes to some of the points below, it is highly beneficial: Proficiency in programming languages, preferably Python. Capable of analyzing large datasets Knowledge of AI and machine learning
-
analysis, statistical modelling, linear mixed models, and machine learning among others. The position is well suited for an individual interested in quantitative genetics and data analysis that wishes