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04.06.2025 Application deadline: 06.07.2025 The Hector Research Institute of Education Sciences and Psychology at the University of Tübingen invites applications for the following positions to be
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, from September 2025 a Postdoctoral Researcher – Longitudinal Proteomic Analysis (Wet Lab & Data Science) Reference number: 2025-0174 The Division of Medical Physics in Radiation Oncology develops new
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and spoken proficiency in both English and German Preferred qualifications Experience in managing collaborative research networks Familiarity with data privacy regulations, open science practices, and
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PostDoc in "Geopolitical dilemmas for management: transfer of governance for the Southern Ocean" ...
Ocean: Ecosystems, sustainability and competing interests at the edge of the world" covering natural and social science perspectives, that will cohere around the ‘dilemma’ facing the Southern Ocean: how
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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Mainz (JGU) is one of the largest universities in Germany. Thanks to its location in the Rhine-Main science region, the university can unfold to its full potential and showcase its innovative power and
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, paragraph 1 of the Act on Fixed-Term Employment Contracts in Academic (WissZeitVG). Your qualifications: Completed doctorate in economics (or near completion) Background in energy or industrial economics with
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and function of matter, from the smallest particles of the universe to the building blocks of life. In this way, DESY contributes to solving the major questions and urgent challenges facing science
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
Ocean: Ecosystems, sustainability and competing interests at the edge of the world" covering natural and social science perspectives, that will cohere around the ‘dilemma’ facing the Southern Ocean: how
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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