Master Thesis - Transfer Learning on Time-Series-to-Image Representations

Stellenbeschreibung:

Overview

The Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG carries out research in the fields of integrated energy infrastructures, geothermal energy and sector coupling for a successful energy transition at seven locations. We develop ideas, technologies and strategies for the next phase of the transformation of the energy systems and see ourselves as independent pioneers in politics, economics, regulation and society. By founding Fraunhofer IEG, the Fraunhofer Society is making a significant contribution to exploiting the markets in a more targeted way for the use of geothermal energy systems, the storage of energy sources and technologies to couple the energy sectors of heat, electricity and transportation.

Machine learning and deep learning methods are increasingly applied to complex data across various domains, including energy systems, industrial processes, healthcare, finance, and mobility. We are offering a Master’s thesis (or research assistant opportunity) in the field of applied machine learning with a focus on developing, implementing, and evaluating modern ML and deep learning approaches for real-world data.

The project can focus on topics such as supervised and unsupervised learning, time-series analysis, computer vision, representation learning, anomaly detection, forecasting, or multimodal data processing. The goal is to design and evaluate robust and efficient machine learning solutions for practical applications, improving performance, generalization, and interpretability.

We are looking for a student assistant at our location in Bochum.

What you will do

Literature review of relevant machine learning methods and state-of-the-art approaches.
Data preprocessing, feature engineering, and exploratory data analysis.
Development and implementation of ML/DL models (e.g., neural networks, transformers, tree-based models).
Training, hyperparameter tuning, and evaluation of models.
Application of developed approaches to real-world datasets and use cases.
Documentation and presentation of results.

What you bring to the table

Enrolled Student in Engineering, Mathematics, Computer Science, or other related STEM programs.
Strong understanding of mathematical modeling and simulation of dynamical systems, with a focus on time-series analysis.
Experience with machine learning and deep learning methods.
Proficient programming skills in Python, with experience in libraries such as TensorFlow or PyTorch for implementing deep learning models.

What you can expect

Practice-oriented work environment that complements your studies with an attractive remuneration.
Supervisors who will strengthen and support you to become successful.
Targeted and individual guidance and mentoring.
Well-equipped technical infrastructure, flexible working hours and possibility to work remotely.

We value and promote the diversity of our employees\' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability.Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.