Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- Chalmers University of Technology
- Chalmers tekniska högskola
- KTH Royal Institute of Technology
- Lunds universitet
- SciLifeLab
- Stockholms universitet
- Karolinska Institutet (KI)
- University of Lund
- Uppsala universitet
- chalmers tekniska högskola
- Chalmers Tekniska Högskola
- Karolinska Institutet
- Linköping University
- Linköping university
- Linköpings University
- Linköpings universitet
- Nature Careers
- Sveriges Lantrbruksuniversitet
- Umeå University
- Umeå universitet
- 10 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
-
emulators for accelerated forward modeling Advanced data-intensive machine learning and AI techniques for survey analysis Applications to major international surveys, including LSST (Rubin Observatory
-
machine learning models in simple, standalone devices that are capable of advanced processing. Building on our work on solution-based neuromorphic classifiers (https://doi.org/10.1002/advs.202207023
-
measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some
-
for molecular dynamics (MD), slashing computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with
-
strategies: Leveraging traditional and causal machine learning approaches to determine which patients are most likely to benefit from specific therapies. Digital pathology and image-based analyses (starting
-
/department-of-computing-science/ Project 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
-
Railway Engineering is seeking a motivated and collaborative postdoctoral researcher for a project on developing machine learning tools for pavement management. The project is conducted in collaboration
-
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
-
to the development of the research milieu. Requirements PhD degree in a field closely related to the position (e.g., computerized image analysis/processing, machine learning, artificial intelligence, data science