Index

Multi-Task Deep Learning for Parkinson’s Disease: Classification and Severity Estimation via Smartwatch Data

[Research Project] Learning-Based Robot Trajectory Prediction

Research Project on Learning-Based Robot Trajectory Prediction

Predicting robot trajectories is critical for safe navigation, swarm coordination, and autonomous decision-making in dynamic environments. This project focuses on developing and evaluating advanced deep learning models for trajectory prediction using sequential data from mobile robots. We will explore recurrent and attention-based architectures such as RNNs, LSTMs, Transformers, and state-of-the-art models like Mamba to build robust and accurate trajectory forecasting systems.

Your profile and skills:

Proficiency in Python and deep learning frameworks (PyTorch)
Strong interest in time-series modeling, sequential data analysis, and physics-based motion dynamics
Familiarity with robotics, motion planning, or control is a plus
Ability to work analytically and approach problems in a structured, research-oriented manner
Independent working style with a passion for team collaboration
Excellent communication skills in English

Application:
Please send your CV, transcript of records, and a short motivation letter describing your interest in robot trajectory prediction and sequential deep learning models to prajol.shrestha@fau.de with the subject line: “[Learning-Based Robot Trajectory Prediction Project Application 2025]”. Applications without these documents will not be considered.

Dual Domain Swin Transformer for Sparse-View CT Reconstruction

The resolution of medical images inherently limits the diagnostic value of clinical image acquisitions. Obtaining high-resolution images through tomographic imaging modalities like Computed Tomography (CT) requires high radiation doses, which pose health risks to living subjects.

The main focus of this thesis is to develop a unified deep learning pipeline for enhancing the spatial resolution of low-dose CT scans by refining both the sinogram (projection) domain and the reconstructed image domain. Leveraging the Swin Transformer architecture, the proposed approach aims to generate high-resolution (HR) scans with improved anatomical detail preservation, while significantly reducing radiation dose requirements.

Deep learning-based boundary segmentation for the detection of a retinal biomarker in volume-fused high resolution OCT

Some of the main causes of vision loss are eye diseases such as age-related macular degeneration (AMD), diabetic retinopathy and glaucoma. Detecting these conditions early is critical and one of the main imaging modalities used in ophthalmology is optical coherence tomography (OCT). This thesis uses high resolution OCT images acquired at the New England Eye Center, Boston, MA. Existing motion correction and image fusion methods are used to generate high-quality volumetric OCT data (Ploner et al., 2024).

Building upon this data, this master thesis includes the development of boundary segmentation for multiple retinal layers, with specific focus on the anterior boundary of the ellipsoid zone. Additionally, the segmentation will be integrated in a pipeline for automated quantification of a biomarker.

The main tasks are:
● Evaluation of a promising new architecture for boundary segmentation, with particular consideration given to the Vision Transformer (Dosovitskiy et al., 2020)
● Development and evaluation of a method for automated quantification of an eye disease biomarker based on the segmented boundaries

Special attention will be given to the following aspects:
● Label efficiency, achieved either through task-specific pretraining or by utilizing a relevant foundational model, such as those proposed by Morano et al. (2025)
● Utilization of 3D data

The resulting model will be compared with the ground truth of the held-out test set. In addition, it will be evaluated against existing U-Net based boundary regression methods, such as those from He et al. (2019) and Karbole et al. (2024). The evaluation uses common regression metrics such as mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE).

The aim of this thesis is to contribute a model for the segmentation of retinal layer boundaries in OCT images, laying the groundwork for the automated quantification of a biomarker for AMD. This thesis shall provide a step towards earlier diagnosis, better monitoring of disease progression and improved clinical workflows.

References
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020, October 22). An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. arXiv.org.
He, Y., Carass, A., Liu, Y., Jedynak, B. M., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L. (2019). Fully convolutional boundary regression for retina OCT segmentation. Lecture Notes in Computer Science, 120–128.
Morano, J., Fazekas, B., Sükei, E., Fecso, R., Emre, T., Gumpinger, M., Faustmann, G., Oghbaie, M., Schmidt-Erfurth, U., & Bogunović, H. (2025, June 10). MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis. arXiv.org.
Karbole, W., Ploner, S. B., Won, J., Marmalidou, A., Takahashi, H., Waheed, N. K., Fujimoto, J. G., & Maier, A. (2024c). 3D deep learning-based boundary regression of an age-related retinal biomarker in high resolution OCT. In Informatik aktuell (pp. 350–355).
Ploner, S. B., Won, J., Takahashi, H., Karbole, W., Yaghy, A., Marmalidou, A., Schottenhamml, J., Waheed, N. K., Fujimoto, J. G., & Maier, A. (2024, May 5–9). A reliable, fully‑automatic pipeline for 3D motion correction and volume fusion enables investigation of smaller and lower‑contrast OCT features [Conference presentation]. Investigative Ophthalmology & Visual Science, 65(7), ARVO E‑Abstract 2794904.