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Field
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, brain circuit imaging and atlas mapping, and research software engineering - studying the brain circuits underlying craving, reward, and addiction. The hire will develop ML/AI methods for an integrated
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perspective Familiarity with design-based, quantitative, qualitative, or mixed-methods educational research A solid foundation in digital research and/or learning technologies, experience working with
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structural systems and ULS / SLS design concepts Experience in structural dynamics and/or vibration analysis is an added asset Familiarity with finite element modelling (e.g. ABAQUS) and probabilistic methods
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and a methodical approach to handling clinical data and biological samples. Previous research experience in traumatic brain injury, neurodegeneration, extracellular vesicles or neurological disease will
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methods and difference-in-differences methods Clear, concise written and verbal communication skills. Ability to work independently and manage time across multiple research tasks. We offer an occupational
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. Selection methods and criteria IV.1. Selection Methods: Assessment of scientific background and CV [ABC (90%)] and Interview [I (10%)]. IV.2. In the assessment of the candidate’s background and CV, scientific
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purification of enzymes. Preparation and analysis of samples using various analytical methods, in particular small molecule liquid chromatography-mass spectrometry (LC-MS/MS), quantitative PCR (qPCR), RNA-Seq
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: Hold a PhD in History or Economic History. Have experience of working with both quantitative and qualitative research methods. Have published research relevant to the GLOW research project. Demonstrate
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duration, but will include anaerobic cultivation of bacteria, single cell RNA sequencing, and communication with collaborators. Your profile The applicant must have expertise in molecular methods and be able
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The future of software engineering is AI-native. Our team works to shape that future through pioneering research, practical engineering methods, and close collaboration with government, industry