Data Science Researcher

Key Responsibilities

• Develop and implement machine learning algorithms, including neural networks, to optimize mechanical engineering processes and predict system behaviors.
• Use Python programming and libraries (such as TensorFlow, PyTorch, SciPy, pandas) to design and test machine learning models for Finite Element / Finite Volume simulations.
• Collaborate with mechanical engineering teams to integrate data-driven solutions into product designs and simulations.
• Analyze large datasets from simulations, experiments, and real-world systems to derive meaningful insights for design optimization and predictive maintenance.
• Build and fine-tune predictive models to improve the accuracy and performance of engineering products.
• Conduct data pre-processing, feature engineering, and model validation to ensure high-quality results.
• Prepare reports and visualizations to communicate complex findings to technical and non-technical stakeholders.
• Stay updated with the latest advancements in machine learning and data science, particularly as they apply to mechanical engineering.

Qualifications

• Experience: 3-5 years of experience in data science or machine learning, with a strong background in mechanical engineering or a related field.
• Education: Master’s degree in Mechanical Engineering, Data Science, Applied Mathematics, or a related discipline. PhD Candidates are welcomed.
• Technical Skills:
  • Proficiency in Python programming and machine learning libraries such as TensorFlow, Keras, PyTorch, and Scikit-learn.
  • Strong understanding of neural networks, deep learning, and machine learning algorithms.
  • Knowledge of mechanical engineering concepts, such as structural mechanics, thermodynamics, or fluid dynamics.
  • Familiarity with data manipulation and visualization tools (e.g., pandas, NumPy, matplotlib, seaborn).