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Schrödinger equations, based on nonlinear approximations such as low-rank tensor decompositions, Gaussian mixtures and neural networks, with a particular focus on the computational costs of reliable solvers
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of Computation, Information and Technology at the Technical University of Munich (TUM) welcomes applications for a PhD or Postdoc Position (m/f/d, 100%, 2 years+) in Numerical Mathematics. Field of Research
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
findings at academic venues and publish research in peer-reviewed journals Your profile # Completed university studies (PhD) in the field of Physics, Computer science, Materials science, Chemistry, or a
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Max Planck Institute for Mathematics in the Sciences | Leipzig, Sachsen | Germany | about 17 hours ago
Researcher The position is being offered within the research project "Combinatorial and Implicit Approaches to Deep Learning”, which is part of the Priority Programme "Theoretical Foundations of Deep Learning
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Physical Oceanography and Instrumentation, Marine Chemistry, Biological Oceanography, Marine Geosciences, and Marine Observations works interdisciplinary within a joint research program. What will be your
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number: 2025-0107 We have an open postdoctoral position in the Division “Personalized Medical Oncology” (head: Prof. Sonja Loges MD, PhD). The Department “Personalized Medical Oncology” focusses
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network
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consortium partners Your Profile: Excellent PhD in electrical engineering, computer science, or a related field Proven track record of high-level research, demonstrated through peer-reviewed publications and
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side projects and build networks inside and outside the institute Analyze diverse data sets for multi-omics integration in plant genetics Expand expertise and collaborations beyond plant research Our Lab
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species Perform bioinformatics and computational analyses to identify microbiome-lifespan relationships Apply rigorous molecular biology methodologies to elucidate mechanisms underlying microbiome effects