2 Rmse Jobs

Job Description Design and develop machine learning models tailored to mechanical engineering challenges, including predictive modelling, simulation optimisation, and failure analysis. Utilise deep learning and other advanced ML techniques to improve the accuracy and efficiency of CAE simulations. Preprocess and analyse large datasets from CAE simulations, experimental tests, and manufacturing processes for modelling. Train, validate, and fine-tune machine learning models using real-world engineering data. Optimise models for performance, scalability, and robustness in production environments. Collaborate with CAE engineers to integrate ML models into existing simulation workflows (e.g., FEA, CFD, structural analysis). Automate repetitive simulation tasks and enable predictive analytics for design optimisation. Work closely with mechanical engineers, data scientists, and software developers to identify business challenges and develop data-driven solutions. Deploy machine learning models into production environments and monitor their performance. Maintain and update models to ensure reliability and continuous improvement. Stay abreast of the latest advancements in machine learning, AI, and CAE technologies. Apply innovative approaches to solve complex engineering problems. Requirements Bachelors or Master’s degree in Mechanical Engineering, Computer Science, or a related field Proven 2-3 years of experience in developing and deploying machine learning models, preferably in mechanical engineering or CAE domain Hands-on experience with CAE tools such as ANSYS, Abaqus, or similar FEA/CFD software Strong programming skills in Python, R, or Java Proficiency in machine learning frameworks (TensorFlow, PyTorch, scikit-learn) Experience with data preprocessing, feature engineering, and statistical analysis Solid understanding of mathematics, statistics, and problem-solving skills Excellent analytical thinking and ability to tackle complex engineering challenges Strong communication and teamwork skills to collaborate across disciplines Preferred: Experience with physics-informed machine learning and digital twin technologies Preferred: Familiarity with automation of CAE workflows and predictive modelling for product design Benefits Challenging job and a chance to team up with a young and dynamic professional group Chance to build yourself as WE grow. Remuneration that stays competitive and attractive to retain the best. Opportunity to join an organization experiencing year on year growth.

Job Description Design and develop machine learning models tailored to mechanical engineering challenges, including predictive modelling, simulation optimisation, and failure analysis. Utilise deep learning and other advanced ML techniques to improve the accuracy and efficiency of CAE simulations. Preprocess and analyse large datasets from CAE simulations, experimental tests, and manufacturing processes for modelling. Train, validate, and fine-tune machine learning models using real-world engineering data. Optimise models for performance, scalability, and robustness in production environments. Collaborate with CAE engineers to integrate ML models into existing simulation workflows (e.g., FEA, CFD, structural analysis). Automate repetitive simulation tasks and enable predictive analytics for design optimisation. Work closely with mechanical engineers, data scientists, and software developers to identify business challenges and develop data-driven solutions. Deploy machine learning models into production environments and monitor their performance. Maintain and update models to ensure reliability and continuous improvement. Stay abreast of the latest advancements in machine learning, AI, and CAE technologies. Apply innovative approaches to solve complex engineering problems. Requirements Bachelors or Master’s degree in Mechanical Engineering, Computer Science, or a related field Proven 2-3 years of experience in developing and deploying machine learning models, preferably in mechanical engineering or CAE domain Hands-on experience with CAE tools such as ANSYS, Abaqus, or similar FEA/CFD software Strong programming skills in Python, R, or Java Proficiency in machine learning frameworks (TensorFlow, PyTorch, scikit-learn) Experience with data preprocessing, feature engineering, and statistical analysis Solid understanding of mathematics, statistics, and problem-solving skills Excellent analytical thinking and ability to tackle complex engineering challenges Strong communication and teamwork skills to collaborate across disciplines Preferred: Experience with physics-informed machine learning and digital twin technologies Preferred: Familiarity with automation of CAE workflows and predictive modelling for product design Benefits Challenging job and a chance to team up with a young and dynamic professional group Chance to build yourself as WE grow. Remuneration that stays competitive and attractive to retain the best. Opportunity to join an organization experiencing year on year growth.