155 data-"https:"-"https:"-"https:"-"https:"-"https:"-"LGEF" Postdoctoral positions at Nature Careers
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for advanced microscopy You will perform live-cell imaging experiments and analyze data You will collaborate with other members of the team in advance microscopy and super-resolution techniques You will develop
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research management and in leading project staff • Didactic skills / experience in e-learning • IT skills and excellent MS Office skills, dat • Knowledge of: mass spectrometry and analysis of complex data
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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a
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reactors Maintain detailed records of experimental data, process conditions, and system modifications. Publish scientific articles based on data collected during the research, development, and innovation
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technologies to analyze the molecular heterogeneity and connectivity of spinal neurons, explore how descending systems in the brain convey information to the spinal motor system, and define the functional roles
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links to representative publications on PubMed) Summary of your current research and key accomplishments Names and contact information of three references We welcome applicants from diverse backgrounds
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discovery, generating mechanistic data that directly informs the development of next‑generation medicines for neurological disorders Base Salary, Academic Level A $113,400 - $121,054 + 17% superannuation
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
and fastest-growing fishery in the Southern Ocean, regulated by the CCAMLR, which aims to manage the fishery sustainably, relying on ecosystem-based approaches incorporating data on predator populations
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to applicants who have received their Ph.D. degrees in neuroscience or related fields within the last 3 years and have experience in imaging in vivo. Skills in computational neuroscience and data mining using
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be