147 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
slowdown at the glass transition, remains a major computational challenge. This Doctoral student project addresses this by combining generative AI models and machine-learned interatomic potentials
-
and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
-
includes implementing and testing machine learning algorithms on quantum control tasks such as state preparation and qubit reset. You will gain hands-on experience with machine learning techniques and their
-
application: Experience in system identification and machine learning is a merit. What you will do Perform research, developing your own scientific concepts and communicating the results of your research
-
your application: A doctoral degree in automatic control, electrical engineering, computational materials science or related. Research experience in battery tests, machine learning, data-driven
-
it effect engagement and learning. For more information about the Akelius Math Learning Lab, see: https://www.chalmers.se/institutioner/mv/akelius-math-learning-lab/ Who we are looking
-
technology, driven by high-quality research and education, openness and collaboration. As a Teaching Fellow, you will contribute to this goal through engaging teaching and learning in a collegial and inclusive
-
Engineers. The offices also host computer infrastructure and machine learning/data science/research data management experts, who develop, build, and manage the local and national resources for large-scale
-
deployments or data collection in real-world environments) Familiarity with current AI technologies (e.g., machine learning, large language models) and an interest in their application to embodied systems. What
-
metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods