In the context of a research project in the Excellence Cluster “Science of Intelligence”, we are offering a student assistant position (60-80 hours/month). In our research project “Developing exploration behavior”, we investigate the development of intelligent behavior, using high resolution behavioral tracking data. We are particularly interested in how behavioral variation arises through development from identical start points, and we employ a unique biological model system, a fully clonal fish species, the Amazon molly, that allows us to work with a large number of identical twins, tracked from day 1 of their life. The main research aim is to understand the rules generating individual-level variation through development, and we employ high resolution tracking data to uncover such rules. This involves the organization and analysis of massive amounts of data spanning entire early life period of individuals. We aim to apply sophisticated unsupervised learning algorithms to this dataset in order to classify behavioral motifs present in our data. 

The specific tasks for the research assistant in the project are: 

– Literature search 

– Experimental data revision and processing; 

– Support in computational model implementation, testing, and large-scale numerical simulations 

– Support in building data pipelines for the analysis of big data 

We seek for motivated students with strong computer skills with a background in computer science, (bio)physics, (bio)informatics, or related natural or engineering sciences and interest in computational biology and/or biological big data. Applicants should have the corresponding proven skills: 

– very good general computer skills (Office, Presentationsoftware) 

– good programming skills (Python, C/C++ or comparable) 

– very good English skills (oral and written) 

Further, previous experience in either of the following areas are strong additional assets: 

– Big data analytical tools 

– Supercomputing/cluster computing 

– Familiarity with some of the following Python libraries: scikitlearn, matplotlib, Numpy, TensorFlow, etc. 

– Familiarity with unsupervised machine learning techniques 

– GitHub 

Please send you application (short motivation letter mentioning also your relevant skills and previous experience, CV, transcript of records) until 2.11.2021 via Email to: Dr. Sean Ehlman, Humboldt University, seanehlman@gmail.com