24 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL" uni jobs at Nature Careers in Germany
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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Assistant Professor of Machine Learning in Digital Health (salary group W1
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team — comprising biologists, engineers, computer scientists, and medical researchers — develops next-generation computational models to interpret complex biomedical data across multiple scales. Our
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. Responsibilities include: Managing and mentoring a multidisciplinary team of machine learning and image analysis experts Coordinating DCU activities across partner sites and aligning them with HI strategy Co
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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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, 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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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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tumor cells and host or immune cells in the tumor microenvironment and periphery to identify predictive biomarkers and develop novel immunotherapeutic approaches for solid tumors (e.g., TCR T cells, CAR T