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scientific programming and numerical / statistical analysis of simulated and observed data. Candidates should be able to demonstrate motivation and a strong eagerness to learn, and have the ability to both
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, including advice for next steps. Your qualities You have At least an MSc degree related to animal health sciences, animal sciences, veterinary sciences, including knowledge about animal feed and natural
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weather prediction using Machine Learning approach (hybrid forecast). The app is also expected to be equipped with seasonal forecast for agricultural planning. You will co-design the short-, medium-, and
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on this project, and therefore, collaborative work between the two successful candidates is expected. The candidate is also expected to contribute to teaching for the MSc course in ornithology of the University
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to contribute to teaching for the MSc course in ornithology of the University Gaston Berger, in Senegal, by dedicating up to 2-weeks per year of their time. In addition, the candidate is expected to offer MSc
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., neurolinguistics, speech therapy, psychology), experience with neuroimaging or willingness to learn (i.e., navigated Transcranial Magnetic Stimulation, Direct Electrical Stimulation), and capacity to gather and
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technologies. The successful candidate will have strong data-driven methodological learning opportunities with high social impact on cancer care organisation. They will work within an interdisciplinary team
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health behaviour. Using a novel combination of deep learning, street view imagery, and epidemiological methods, we aim to identify the most effective urban exposure modifications. This research will
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for this position will have the following qualifications/qualities An MSc degree in chemistry or a related field. Very strong academic performance. Experience in molecular machine learning. Experience with
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interdisciplinary research activities. Job requirements We are looking for an enthusiastic and motivated candidate with an MSc degree in clinical epidemiology, (health) economics, econometrics, medical statistics