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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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and simulation aspects across a wide range of fields - from biomechanics and geophysics to polymer-fluid coupling. Further areas of interest include numerical algorithms for high-dimensional problems
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project team on “Participatory Algorithmic Justice: A multi-sited ethnography to advance algorithmic justice through participatory design” (PARTIALJUSTICE) to examine issues of justice and participation in
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integration. Role: Apply ML to barley genomic data, emphasizing gene regulation and genetic variation Collaborate with geneticists, breeders, and industry partners to move research toward applications Explore
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that algorithmic parameters are tuned so that the over-approximation of the computed reachable set is small enough to verify a given specification. We will demonstrate our approach not only on ARCH benchmarks, but
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research group study the genetic and epigenetic changes that drive the development of hematological malignancies by employing cutting-edge multi-omics approaches. The successful candidate will join a dynamic
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-sampling data. Furthermore, the position holder will play a central role in creating high-quality training datasets (seagrass maps) to support artificial intelligence (AI) algorithms used in related projects
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 3 months ago
links between family and health, [2] the role of genetic factors in shaping health inequalities, [3] international comparisons of health and health inequalities, and [4] developing new methods, in
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage
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populations. This project will explore the genetic and evolutionary mechanisms shaping adaptation through a combination of genomic, computational, laboratory, and field-based approaches. Research focus