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| = Master and Bachelor Thesis Supervision = | = Bachelor and Master Thesis Opportunities at TU Berlin's Machine Learning Group = |
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| This page is targeted at both students and staff. == General procedure == After the topic and supervisor have been found (see below), the student must write a proposal with '''preliminary''' results. After Klaus approves the proposal, the student can register the thesis, and the writing period begins (typically three months for BA and six months for MA). This implies that a thesis often requires more time than the official writing period. It might be possible to hand in the thesis early (check with the respective examination regulations for the student’s degree program). However, we encourage significant time between registration and submission. Though we encourage thesis supervision, there is no obligation to supervise every interested student, e.g., in case no appropriate topic/supervisor is available. == Unsolicited applications == Students can proactively contact researchers if none of the open topics below are suitable. It is possible to suggest your own topics or ideas. In general, being friendly, motivated, and skillful is helpful. Though our group tries to supervise as many students as possible, we do not always have the capacity to supervise every interested student. The senior researchers have a broad overview of current research in our group: * ML for Quantum Chemistry: Stefan Chmiela (stefan@chmiela.com) * Kernel Learning: Stefan Chmiela (stefan@chmiela.com) * Explainable AI: Grégoire Montavon (gregoire.montavon@tu-berlin.de) * Anomaly detection/Unsupervised learning: Robert Vandermeulen (vandermeulen@tu-berlin.de) * Probabilistic/Bayesian models: Shinichi Nakajima (nakajima@tu-berlin.de) * Time series analysis and signal processing: Andreas Ziehe (andreas.ziehe@tu-berlin.de) * Multimodal signal acquisition and analysis - neurotechnology + biomedical sensing: Alexander von Lühmann (vonluehmann@tu-berlin.de) The junior researchers are typically more specialized: * Jannik Wolff (wolff [dot] jannik [at] icloud.com): Learning with multiple modalities == Open topics == * Please delete topics that are not available anymore. * Please include the entry's date. This can help identify outdated topics that should have been deleted. * The last column can entail '''short''' additional information helpful to students to assess their interest in a topic without contacting the person. For example, you may include a short description or a link to a relevant paper. || '''MS/BS''' || '''Topic''' || '''Supervisor + email address''' || '''Date of entry''' || '''Additional information'' || || BS || Assessment of data quality in open source wind turbine SCADA data sets || Simon Letzgus (simon.letzgus@tu-berlin.de)|| Jan 2023 || - || || MS || Dynamic visualization of XAI for regression models || Simon Letzgus (simon.letzgus@tu-berlin.de)|| Jan 2023 || - || || MS || Adaptation of generic to subject specific headmodels using ultrasound and photogrammetry || Alexander von Lühmann (vonluehmann@tu-berlin.de) || Feb 2023 || - || || MS || Automatic naturalistic stimulus detection using combined eye-tracking and computer vision || Alexander von Lühmann (vonluehmann@tu-berlin.de) || Feb 2023 || - || || MS || Modelling the effect of motion on systemic physiology in the head measured by fNIRS, Microsoft Kinect, and Accelerometers || Alexander von Lühmann (vonluehmann@tu-berlin.de) || Feb 2023 || - || || MS || XAI for distribution shift in malware detection || Daniel Arp (d.arp@tu-berlin.de), Lorenz Linhardt (l.linhardt@campus.tu-berlin.de) || Feb 2023 || https://www.overleaf.com/read/hfgvhwsnjpxk || |
[[https://web.ml.tu-berlin.de/teaching/thesis/|Our new website]] gathers all information about thesis opportunities. |
Bachelor and Master Thesis Opportunities at TU Berlin's Machine Learning Group
Our new website gathers all information about thesis opportunities.