Your contribution to something big:
You will contribute to an industrial research project focused on the development and optimization of heat pump systems. You will combine machine learning, data analysis, modelling, and experimental testing to develop predictive models based on experimental and field data, supporting the characterization and optimization of key parameters of the heat pump refrigeration cycle. Your main responsibilities will be:
Machine Learning Development: Develop, evaluate, and improve machine learning models using experimental and field data from heat pump systems.
Data Analysis & Modelling: Analyse data, model and simulate dynamic systems, and develop numerical approaches to address complex engineering problems.
Heat Pump Optimization: Support the development and optimization of refrigeration cycles and heat pump systems using data-driven and model-based approaches.
Predictive Models: Apply data-based methods to improve model-based concepts and support the analysis and optimization of constrained linear and nonlinear systems.
Experimental Testing & Data Acquisition: Define and prepare experimental data acquisition systems, conduct tests, and analyse the resulting data to support model development and engineering decisions.
Test Planning: Define and prepare structured test plans to generate high-quality experimental data for predictive model development and improvement.
Field Data Analysis: Use available field data to analyse heat pump performance and support the optimization of control parameters.
Machine Learning Assessment: Investigate the benefits, limitations, and risks of machine learning models, including their potential to reduce testing effort and support heat pump development.
What distinguishes you:
Education: University degree in Mechanical Engineering, Electrical Engineering, Mathematics, Computer Science, or a comparable technical field.
Machine Learning: Good understanding of machine learning models and data-driven engineering methods, with the ability to apply them to engineering problems.
Programming: Good programming skills in Python and/or MATLAB for data analysis, modelling, simulation, or machine learning applications.
Modelling & Simulation: Understanding of dynamic systems, numerical modelling, and simulation, with the ability to analyse complex engineering problems using model-based and data-driven approaches.
Data Analysis: Experience working with experimental and/or field data, including data processing, analysis, and interpretation.
Testing & Experimentation: Understanding of testing methodologies and data acquisition, with the ability to prepare test plans and support experimental activities.
Digital Tools: Good knowledge of Microsoft Office tools, particularly Excel, Word, PowerPoint, and OneNote. Experience with MATLAB and/or Simulink in engineering or laboratory environments.
Problem Solving: Strong analytical and problem-solving skills, with a results-oriented mindset and attention to detail.
Working Style: Proactive and responsible, with the ability to independently organize your work, take ownership of assigned activities, and propose solutions.
Teamwork & Communication: Good communication skills and the ability to work effectively in a collaborative and Agile environment.
Languages: Excellent written and spoken English.
Work #LikeABosch includes:
⚖️ Flexible work conditions
🔀 Hybrid work system
🌐 Exchange with colleagues around the world
🧑⚕️ Health insurance and medical office on site (general surgeon, psychology, physiotherapy, general clinic)
📚 Training opportunities (p.e., technical training, foreign languages training) & certifications
📈 Opportunities for career progression and continuous professional development
💲 Access to great discounts in partnerships and Bosch products
🏋️ Sports and health related activities
💰 Flexible benefits platform
🅿️ Free parking lot
🍽️ Canteen
Success stories don´t just happen. They are made...
Make it happen! We are looking forward to your application!