211 programming-"the"-"DAAD"-"U"-"Prof"-"EURAXESS" Postdoctoral positions in United Kingdom
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context. Able to plan and prioritise workload and keep clear and well-organised laboratory notes and records. Desirable criteria Background in cardiovascular biology Expertise with studying autophagy
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be appointed as Research Assistant. Expert in programming on supercomputers Expert in computational fluid dynamics Experience collaborating with industry partners and diverse research teams Creative
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to contribute to broader management/administration processes Contribute to the planning and organising of the research programme and/or specific research project Co-ordinate own work with others to avoid conflict
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neuroscience or a related field. Experience of MEG/OPM-MEG acquisition and data analysis is essential. Experience with MATLAB or Python computer programming for neuroimaging data analysis is essential
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data communication (assessed at: application/interview) Ability to work under own initiative to agreed protocols and deadlines, and to plan and progress work activities (assessed at: application
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developmental science. The successful candidate will contribute to a major research programme investigating how educational experiences shape mental health from childhood into adulthood. The role involves working
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of these approaches, and how to generate robust inferences. We would expect you to be highly skilled at using programming languages such as R or Python. It is important for you to have a track record of implementing
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specialist knowledge in mechatronics design and control, and proven programming experience in Python, MATLAB or C/C++. Informal enquiries may be addressed to Professor Liang He (email: liang.he@eng.ox.ac.uk
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research centre (Hub) funded by EPSRC for a 5-year research programme into Integrated Quantum Networks (IQN). The IQN Hub is supported by an initial £21.8M award from UKRI and additional industrial support
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
related field. Strong quantitative and programming skills (e.g., R required). Experience in ecological modelling with different Artificial Intelligence methodologies, including but not limited to machine