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Field
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exceptional researchers to join our dynamic team. As a Machine Learning Researcher, you will apply advanced ML techniques to a wide range of forecasting challenges, including time series analysis, natural language
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journals is desired Ability to work reliably and collaboratively in a scientific research setting Strong command of spoken and written English; German language skills are a plus. We offer Involvement in a
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identification of spider mite infestation foci and needs-based beneficial insect application in outdoor cucumber cultivation’, which is funded by the BMBF within the funding programme KMU-innovativ: Bioökonomie
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located in Central Cambridge, Cambridgeshire, UK. The key research and scholarship responsibilities and duties of this role are: Plan and manage research activities Support an investigation into carbon
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research with possible continuation as a research assistant or in a possible PhD degree programme Place of employment and place of work The place of work is the Department of Animal and Veterinary Sciences
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cancer biology, demonstrated by publications in high impact journals. The person would be well versed with various techniques used in the field of cancer immunology as well as having the ability to plan
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located in Central Cambridge, Cambridgeshire, UK. The key research and scholarship responsibilities and duties of this role are: Plan and manage research activities. Support an investigation into carbon
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the consistent further development of its profile-forming priority research areas Molecular Cell Biology, Neurosciences, Cardiovascular Medicine and Oncology. With its German Centers for Health Research partner
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Organisation Job description As a researcher, you will play a key role in developing and implementing the DNPP’s research programme. Your responsibilities will include conducting independent and
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yourself quickly with new concepts. Enjoyment of scientific work. Very good/good programming skills (at least in Python and C++). Good written and spoken German (at least C1 level) and English. In-depth