The positions are part of the NEXCELL research project. Application deadline 22.2.2026. Apply here.

We are looking for student assistants for our AI-related research projects. Please contact sebastian.bader (at) uni-rostock.de

We offer various interesting and challenging topics for theses in the scope of - Artificial Intelligence in general

• Machine learning, in particular artificial neural networks
• Human behaviour analysis
• Assistive systems for people with cognitive and physical impairments
• Natural language processing

• You should be familiar with Latex and GIT
• You should be familiar with working on a linux command line, also remotely via ssh
• You need a login at the computer science department
• You should have passed at least some of the AI-focused lectures offered by the MMIS group successfully

To apply for a thesis, please send an email to Dr. Sebastian Bader containing the following information:

• List of HAIML AI-lectures you have attended, including the final grade achieved by you
• Summary of your study results so far
• Your solution to the self assessment task provided below
• The project (one of the current research projects) you are interested to work in

Before applying for a thesis at the chair of HAIML, please solve one of the following small self assessment tasks to show your skills:
 
1. Signal Analysis

Analyse the following datafiles (SelfAssement1.csv, SelfAssement5.csv). They contain a mixture of three signals each and we would like to know which signals these are and how you analysed the datasets. Please submit your solution as a python script, Jupyter notebook, or R script. Your file has to load the data file, describe your ideas while analysing the data, and finally show a description as well as a plot of the three sub-signals.

2. Neural Networks

Build a convolutional auto-encoder using Keras and train it using the CIFAR10 dataset. Afterwards remove the decoder part and replace it with a classification head, i.e., a fully connected sub-network which maps the latent representation generated by the autoencoder to the output classes provided as true labels from the dataset. Use only a small balanced subset of the labelled training data to train the classification head. The pre-trained encoder should help to create better classification results, compared to a classification network trained on the small labelled dataset. Explain how you created the labelled subset for training, the architectures of the two networks and the evaluation. 

If you are doing your thesis in some company or research institute outside the University of Rostock, you will need a supervisor here. We are accepting co-supervision of your thesis under the following conditions:

• You apply for a thesis as described above
• You send us a detailed topic description as a one-page PDF which contains the general idea, a list of sub-tasks you will have to perform
• Prior to signing the official documents we will have a joint telephone / video conference with your external supervisor and you. You will have to arrange that meeting!
• The company has to allow a joint publication of the results