Student Assistant in Applied Machine Learning
Role details
Job location
Tech stack
Job description
- Develop and test new deep learning architectures for time series with multimodal inputs
- Analyze and preprocess different data types (text, images, etc.)
- Review the literature and relevant datasets
- Orchestrate and document deep learning experiments
- Conduct applied research on exciting projects
- Prepare presentations and papers
- Test software and frameworks
What you contribute
Requirements
- You are currently enrolled in a master's program in Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, or a related field, with a strong academic record
- Proficiency in PyTorch or JAX, including experience training machine learning models on one or more GPUs and the ability to work with existing codebases to set up training runs
- Research interest in one or more of the following areas: probabilistic machine learning, time series forecasting, deep tabular models, or decision trees
- Experience with noisy, real-world datasets
- Solid understanding of machine learning theory
- Strong data engineering and Python programming skills, with working knowledge of NumPy, pandas, SQL, Bash, Docker, and Git
- Experience reading and writing mathematical/statistical analyses
- Excellent spoken and written English
Benefits & conditions
- Attractive salary
- Modern and excellently equipped workspace in a central location
- Great and cooperative working atmosphere in an international team
- Flexible working hours
- Opportunities to work from home
- Opportunities to write a master's thesis (depending on topic suitability)
The position is initially limited to 1 year. An extension is explicitly desired.
The monthly working time is 80 hours. This position is also available on a part-time basis. 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. Our tasks are diverse and adaptable - for applicants with disabilities, we work together to find solutions that best promote their abilities. The same applies if they do not meet all the profile requirements due to a disability. 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.
