Sort by
Refine Your Search
-
Listed
-
Employer
- Nature Careers
- Technical University of Munich
- Leibniz
- Free University of Berlin
- Heidelberg University
- Forschungszentrum Jülich
- University of Tübingen
- Deutsches Zentrum für Neurodegenerative Erkrankungen
- Friedrich Schiller University Jena
- Helmholtz-Zentrum Berlin für Materialien und Energie
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Max Planck Institute for Astronomy, Heidelberg
- Max Planck Institute for Demographic Research (MPIDR)
- Universitaetsklinikum Erlangen
- WIAS Berlin
- 5 more »
- « less
-
Field
-
approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
-
» Computer engineering Researcher Profile First Stage Researcher (R1) Recognised Researcher (R2) Established Researcher (R3) Positions Postdoc Positions Country Germany Application Deadline 30 Sep 2025 - 23:59
-
in machine learning, AI and programming skills, e.g. Python basic knowledge of materials science / materials engineering Leibniz-IWT is a certified family-friendly research institute and actively
-
group (https://bckrlab.org). We focus on high impact applications and work on knowledge-centric AI and biomedical machine learning including multi-omics integration, single cell analysis, and sequential
-
. Job description: - first-principle modeling and simulations of electrolytes - development of new machine learning strategies and quantum simulation approaches - application of specially developed
-
results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
-
, machine learning or causal inference for estimating, understanding and forecasting demographic and health outcomes, at the individual and aggregate levels, including as they relate to life course and socio
-
machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
-
-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
-
reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge