onsite
Working Student for Scientific Computing & Machine Learning - ag
ML Engineer
Support research teams by developing and optimizing scientific computing pipelines and machine‑learning models using Python, NumPy/SciPy, and GPU‑accelerated frameworks. Ideal for a student eager to apply theory to real‑world data challenges.
About the role
Key Responsibilities
- Develop and maintain Python code for numerical simulations and data‑driven research projects.
- Implement, train, and evaluate machine‑learning models with libraries such as PyTorch or TensorFlow.
- Optimize computational workflows for high‑performance computing environments, including parallelization and GPU utilization.
- Collaborate with researchers to translate scientific requirements into scalable software solutions.
- Document code, results, and best practices to ensure reproducibility.
Requirements
- Enrolled in a Computer Science, Engineering, Physics, or related degree program (Bachelor or Master).
- Strong proficiency in Python and scientific libraries (NumPy, SciPy, pandas).
- Experience with machine‑learning frameworks (e.g., PyTorch, TensorFlow) and basic model development.
- Familiarity with high‑performance or parallel computing concepts (MPI, CUDA, multi‑threading).
- Good problem‑solving skills, ability to work independently and in a team, and solid written communication in English.
Skills
pythonnumpymachine learningpytorch