50 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions at Forschungszentrum Jülich in Germany
Sort by
Refine Your Search
-
models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
-
mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time-lapse data Proven programming expertise in
-
, machine learning, energy technology or related subjects Prior experience in building predictive models using regression techniques, neural networks (CNN, GNN) or symbolic regression Experience in
-
neuroscience is essential Experience with modelling, analysis of complex dynamical systems, simulation, analysis of large-scale datasets with machine learning methods, and software development are beneficial
-
sound understanding of data evaluation Prior experience with single-cell data analysis, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently
-
Your Job: As part of an interdisciplinary project team with researchers from bioinformatics you will work on quantum algorithms for drug discovery. Here, the focus lies on machine learning and
-
the collected data in accordance with FAIR standards for collaborative use within the Decode project and for machine learning applications Presentation of results in team meetings and preparation of a
-
. Usadel, specializes in data integration, classical bioinformatics, data science, and machine learning. The offered position will focus on the ELN-RO project, which has the aim to establish seamless
-
skills (Python, R, Java, …) and interest to work in polyglot software environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods
-
institute Supervision from both chemical engineering and machine learning experts, ensuring strong interdisciplinary guidance Your Profile: Current Master`s student in Process Systems Engineering