Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Technical University of Munich
- Nature Careers
- Leibniz
- Forschungszentrum Jülich
- Heidelberg University
- University of Tübingen
- Fritz Haber Institute of the Max Planck Society, Berlin
- Helmholtz-Zentrum Geesthacht
- Max Planck Institute for Dynamics and Self-Organization, Göttingen
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
-
Field
-
theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
-
Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
-
for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
-
at least statistical insights into the risks and success rates of real, patient-specific aneurysms, their treatment options, and long-term prognosis. The project is complemented by contributions in machine
-
to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
-
to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
-
-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
-
sequencing, large-scale genomic, transcriptomic, proteomic, metabolomic, and phenotypic data) using cutting-edge technologies, such as machine learning You will perform transcriptomic and epigenetic analysis
-
existing quantitative methods in international nuclear safeguards with state-of-the-art artificial intelligence/machine learning (AI/ML) algorithms Contribute to standardising methodologies for calculating
-
motivated PhD students, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service