We are currently looking for 1 researcher: https://jobs.uni-rostock.de/jobposting/4a060dbfb56813b0d891cabcf82911cd556eb8230?ref=homepage
Please apply directly via https://jobs.uni-rostock.de/de/jobposting/4a060dbfb56813b0d891cabcf82911cd556eb8230/apply
We are also looking for student assistants for our AI-related research projects. Please contact sebastian.bader (at) uni-rostock.de
BSc, MSc and Pre-Thesis at MMIS Topics
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
How to apply?
To apply for a thesis, please send an email to Dr. Sebastian Bader containing the following information:
- List of MMIS 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
Small Self-assessment task
Before applying for a thesis at the chair of MMIS, 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.
Co-Supervision for external Theses
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