137 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" positions at Nature Careers in Germany
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connecting AI, computational biology, human–computer interaction, and research software engineering. Close collaboration with the Helmholtz AI Consultant Team, providing direct exposure to a broad range of
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of artificial intelligence, machine learning and/or deep learning experience in scientific publishing and presenting research results knowledge or experience in public health research Personal skills Independence
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, engineers, computer scientists, and medical researchers — develops next-generation computational models to interpret complex biomedical data across multiple scales. Our innovations in tissue clearing, 3D
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, engineers, computer scientists, and medical researchers — develops next-generation computational models to interpret complex biomedical data across multiple scales. Our innovations in tissue clearing, 3D
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the organization. Your profile Bachelor's or master's degree in computer science, computer engineering, cybersecurity or a related field and relevant security certifications (e.g., OSCP, CCSP, CISSP, CISM) from a
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biophysics, computational biology, mathematics in the life sciences, computer science and machine learning with application to biological systems, and related areas. What we provide The CSBD provides fully
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Senior Semantics Data Scientist (m/f/d) in the fields of Computer Science, Data Science, Physics, Ma
key role in the foundation of interoperable, machine-readable data platforms, powering AI analyses and automated workflows. You will collaborate with top scientists from all subject areas at BAM
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Professor (W3 / W2 with Tenure Track to W3) for Materials and/or devices for Photonics and Quantum T
quantum technologies. This research can be complemented by digital methods of process simulation and optimization, as well as machine learning. Requirements include an outstanding PhD in materials science
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
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), proteomics (LC-MS/MS), (epi)genomic data processing, multi-omics integration, machine learning approaches for high-dimensional data, confocal / two-photon imaging, tissue clearing and light-sheet microscopy