Student assistant position for Machine Learning to infer disease status of moving individuals

vor 1 Woche

Berlin, Deutschland Leibniz-Institut für Zoo- und Wildtierforschung Vollzeit
Student assistant position for Machine Learning to infer disease status of moving individuals bei Leibniz-Institut für Zoo- und Wildtierforschung | softgarden View job here Student assistant position for Machine Learning to infer disease status of moving individuals
  • Teilzeit
  • Friedrichsfelde, Berlin, Deutschland
  • Hybrid
  • Ohne Berufserfahrung
  • 27.06.24

The Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) in the Forschungsverbund Berlin e.V., a member of the Leibniz Association, aims to understand and, where possible, ‘improve’ the adaptive capacity of wild animals to cope with global change. To this end, it focuses on the diversity of lifestyles, diseases and mechanisms of evolutionary adaptation of mammals and birds, on the limits of these mechanisms in natural and anthropogenically influenced environments, and on conservation strategies that take these into account. The Institute fulfils this mission through long-term, application-oriented, interdisciplinary basic research in evolutionary ecology, ecological dynamics, evolutionary genetics, wildlife diseases, reproductive biology and reproductive management.

In it’s Department of Ecological Dynamics, the IZW is seeking to appoint a

Student assistant position for Machine Learning to infer disease status of moving individuals

The data on moving individuals is collected at increasingly high temporal and spatial resolution. Such technical advances allow tracking several moving individuals simultaneously, which also allows assessing fine-scale differences in movement behavior imposed by pathogen load. In this project we use machine learning methods on labelled data to distinguish between infected and non-infected individuals that would allow infering on disease-induced behavioral differences among moving individuals. The method will be compared to existing statistical algorithms and applied to the field data on moving birds (passerines).

Requirements
  • Registration at a German university (Bachelor/Master) in biology or comparable course of study; Students enrolled in programs focusing on computer sciences, information systems and programming (or similar) with personal interest in biology are encouraged to apply.
  • Previous experience with R or/ and python.
  • High motivation, flexibility and ability to work in a team.
  • Good command of English, written and spoken.
  • Previous experience with machine learning methods (particularly with deep learning algorithms such as artificial neural networks) or agent-based simulations is advantageous.
  • Good organizational skills, high motivation and ability to work independently.

The position will will be preferably filled by the September 1st, 2024 and is initially limited until 31.12.2024. Working hours are a minimum of 10 per week, with salary of 13,68 euros per hour. There is a possibility of prolongation in 2025. The work will be supervised by Prof. Dr. Kramer-Schadt and Dr. Viktoriia Radchuk.

We welcome people of any gender, origin, sexual orientation and religion; there is equal opportunity. Severely disabled persons will be given preferential consideration if they are equally qualified. The Leibniz Institute for Zoo and Wildlife Research is "Total-E-Quality" certified, actively supports the compatibility of work and family and promotes equality.

Enquiries or questions should be directed to Prof. Stephanie Kramer-Schadt (kramer@izw-berlin.de).

Go to Vacancies/Jobs on our homepage (www.leibniz-izw.de) and click on this advertisement and then on "Apply Online". Please send us your complete and informative application documents in this way, i.e. letter of motivation, CV, proof of university enrolment, copies of relevant degrees and names and contact details of one referee. With fully prepared documents, your online application will take 10-15 minutes. Applications received by July 11th, 2024 will be considered in any case.

We are looking forward to your application

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