148 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" positions at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive models and digital decision-support
-
computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
-
optical microcavities, and similar lines, summarized in these publications: https://www.pnas.org/doi/abs/10.1073/pnas.2505144122 https://www.science.org/doi/full/10.1126/sciadv.adn1825 https
-
the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
-
percent of working hours Contract terms The Doctoral student positions is fully funded from start. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which
-
– forward. URL to this page https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?rmpage=job&rmjob=14661&rmlang=UK
-
, in close collaboration with wider society. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. URL to this page https://www.chalmers.se/en/about-chalmers/work
-
-energy devices. Using state-of-the-art electronic-structure calculations and machine learning methods, you will model these effects and contribute to the design of improved semiconductors for solar cells
-
terms The Doctoral student positions are fully funded from start. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which extends the position up to five
-
-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction