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Control Systems Research Engineer - Job Opportunity at BELIMO Automation AG

Hinwil, Switzerland
Full-time
Senior
Posted: June 1, 2025
On-site
CHF 95,000 - CHF 130,000 per year (approximately USD 105,000 - USD 145,000). This estimate reflects the premium for PhD-level research positions in Switzerland's high-cost environment, combined with the specialized nature of embedded AI and control systems expertise. Belimo's position as a global leader in HVAC actuators and the growing strategic importance of AI integration would support compensation at the higher end of this range.

Benefits

Opportunity to collaborate with leading universities and academic institutions, providing access to cutting-edge research networks and publications
Supervision of master's theses and PhD projects, offering significant professional development in academic mentorship
Position at the forefront of HVAC innovation with direct impact on future industry solutions
Cross-functional collaboration with R&D teams, ensuring broad technical exposure and skill development
Access to advanced simulation tools and embedded systems development resources

Key Responsibilities

Lead cutting-edge research in control strategies and reinforcement learning algorithms specifically optimized for embedded systems, positioning the company at the technological forefront
Drive innovation through comprehensive algorithm implementation and validation using both advanced simulations and real-world experimental frameworks
Optimize complex algorithms for real-time performance within the strict resource constraints of embedded devices, directly impacting product competitiveness
Spearhead cross-functional collaboration with R&D teams to seamlessly integrate breakthrough solutions into commercial products
Mentor and supervise academic research projects including master's theses and PhD initiatives with university partners
Maintain strategic awareness of academic and industrial advancements to ensure competitive technological positioning
Create comprehensive technical documentation that bridges academic research and practical implementation
Communicate complex research findings to diverse audiences, influencing both technical development and strategic business decisions

Requirements

Education

Ph.D. in Computer Science, Mathematics, Robotics, Physics, or a closely related field

Experience

Advanced research experience in control theory and reinforcement learning

Required Skills

Deep theoretical understanding of control theory principles (e.g., classical control, modern control, optimal control, robust control) Experience in reinforcement learning, especially deep and model-based methods Strong background in mathematics and numerical methods Proficient with simulation tools like MATLAB, Simulink, or Python environments Skilled in Python and C/C++ for algorithm development Familiar with thermal physics modeling and optimization under constraints Fluent in English
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Sauge AI Market Intelligence

Industry Trends

The HVAC industry is experiencing a significant transformation driven by IoT integration and smart building technologies, creating unprecedented demand for advanced control systems that can adapt to complex environmental variables and energy efficiency requirements. This shift is pushing companies to invest heavily in AI-driven solutions that can optimize building performance in real-time. Reinforcement learning applications in industrial automation are rapidly expanding beyond traditional manufacturing into building automation systems, with particular emphasis on energy optimization and predictive maintenance. The convergence of edge computing and machine learning is enabling more sophisticated control algorithms to run directly on embedded devices. The European Green Deal and similar global sustainability initiatives are driving massive investment in intelligent HVAC systems that can reduce energy consumption by 20-30% through advanced control strategies. This regulatory environment is creating a competitive advantage for companies that can develop sophisticated, embedded AI solutions. There is a growing trend toward model-based reinforcement learning in industrial applications, as it offers better sample efficiency and safety guarantees compared to model-free approaches, which is crucial for real-world deployment in critical building systems.

Role Significance

Typically leads a small research team of 2-4 engineers and researchers, while also coordinating with 8-12 member cross-functional R&D teams. The academic supervision component suggests oversight of 3-5 graduate students at any given time, requiring strong project management and mentorship capabilities.
This is a senior research position that combines individual contributor responsibilities with academic mentorship duties. The role operates at a strategic level within the R&D organization, with direct influence on future product directions and technology roadmaps. The position requires someone who can independently drive research initiatives while collaborating across multiple organizational levels.

Key Projects

Development of next-generation smart actuator control systems that integrate deep reinforcement learning for autonomous HVAC optimization Research initiatives focused on edge AI implementation for real-time building climate control with sub-second response times Collaborative university research projects exploring novel control theory applications in energy-efficient building systems Advanced thermal modeling projects that combine physics-based models with machine learning for predictive climate control

Success Factors

Ability to translate cutting-edge academic research into commercially viable embedded solutions, requiring both deep theoretical knowledge and practical engineering acumen Strong publication record and active engagement with the academic community, as this role serves as a bridge between Belimo and leading research institutions Proficiency in rapid prototyping and iterative development processes, essential for validating algorithms in real-world embedded environments Excellent communication skills for presenting complex technical concepts to diverse stakeholders, from academic partners to commercial product teams Strategic thinking capability to identify research directions that align with market needs and company competitive positioning

Market Demand

Very High - The intersection of embedded systems, reinforcement learning, and HVAC automation represents a critical skill gap in the market. The increasing focus on energy efficiency and smart building technologies has created strong demand for professionals who can bridge academic research with practical embedded implementations.

Important Skills

Critical Skills

Deep reinforcement learning expertise is absolutely essential as the HVAC industry moves toward adaptive, learning-based control systems that can optimize performance across varying environmental conditions and usage patterns. This skill directly translates to competitive advantage in energy efficiency and user comfort. Embedded systems optimization capabilities are crucial because HVAC control devices must operate under strict power, memory, and computational constraints while maintaining real-time performance. The ability to implement sophisticated algorithms within these constraints is a rare and valuable skill. Control theory mastery remains fundamental as it provides the mathematical foundation for stable, predictable system behavior, which is non-negotiable in building automation systems where failure can impact occupant safety and comfort. C/C++ programming proficiency is critical for embedded implementation, as these languages provide the low-level control and efficiency required for resource-constrained devices in HVAC applications.

Beneficial Skills

Thermal modeling expertise becomes increasingly valuable as smart buildings require more sophisticated understanding of heat transfer and energy dynamics for optimal control strategies Academic research and publication experience enhances career prospects and provides credibility when collaborating with university partners and presenting at technical conferences Project management and mentorship skills are valuable for leading research initiatives and developing junior team members in this rapidly evolving field Knowledge of edge computing and IoT architectures is beneficial as HVAC systems become more connected and distributed in smart building ecosystems

Unique Aspects

This role uniquely combines embedded systems expertise with reinforcement learning research, a rare combination that positions the individual at the intersection of two high-growth technical domains
Direct collaboration with leading universities provides access to cutting-edge research and potential for high-impact publications, unusual for most industrial R&D positions
The focus on thermal physics modeling adds a specialized domain expertise that differentiates this role from general AI/ML positions and creates unique career value
Opportunity to influence the future of smart building technology through a market-leading company with global reach and significant R&D investment
The combination of algorithm development and real-world embedded implementation provides comprehensive experience across the full technology stack

Career Growth

Progression to principal-level positions typically occurs within 3-5 years, with advancement to director-level roles possible within 5-8 years depending on research impact and leadership development. The strong academic component of this role provides excellent preparation for either continued industry advancement or transition to senior academic positions.

Potential Next Roles

Principal Research Scientist positions leading larger research initiatives and strategic technology roadmaps R&D Director roles overseeing multiple research streams and managing significant research budgets Chief Technology Officer positions in industrial automation or smart building technology companies Technical leadership roles in AI/ML startups focused on industrial applications or energy efficiency Academic positions as industry professors or research directors at leading technical universities

Company Overview

BELIMO Automation AG

Belimo Automation AG is the global market leader in the development, production, and marketing of actuator solutions for controlling heating, ventilation, and air conditioning systems. Founded in 1975 in Switzerland, the company has established itself as the premier innovator in HVAC actuators, control valves, and sensors, serving customers in over 80 countries through a network of subsidiaries and distributors.

Belimo holds the number one global market position in HVAC actuators with approximately 35% market share, significantly ahead of competitors. The company's focus on innovation and quality has resulted in consistent growth and strong financial performance, with annual revenues exceeding CHF 750 million and a reputation for premium, reliable products in the building automation industry.
This position in Hinwil, Switzerland, places the role at Belimo's global headquarters and primary R&D center. Switzerland's strong engineering education system and proximity to leading European technical universities provide excellent collaboration opportunities. The location offers access to a highly skilled workforce and positions the role within a cluster of precision engineering and automation companies.
Belimo maintains a Swiss engineering culture that emphasizes precision, innovation, and long-term thinking. The company's strong financial position and family-business heritage create a stable environment for long-term research projects. The emphasis on academic collaboration and the company's investment in advanced R&D suggests a culture that values technical excellence and provides resources for meaningful innovation work.
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