Data engineer Jobs in Duderstadt

Summary Statement

The Global Research department at Ottobock is seeking a Control Engineer (d/f/m) with hands-on experience in prosthetics and orthotics to join its research group. The role offers the opportunity to work at the intersection of control engineering, human movement science, and clinical impact within one of the world's leading organisations in prosthetics and orthotics.

The department is committed to developing technologies that measurably improve mobility and quality of life for people who rely on powered prosthetic and orthotic devices. The group focuses on next-generation sensing and control strategies for lower- and upper-limb wearable robots, working across the full pipeline from biomechanical characterisation to embedded control implementation and experimental validation.

The position is located in our headquarter in Duderstadt near Göttingen.

Duties & Responsibilities

• Develop, implement, and validate control strategies for powered lower- and upper-limb prosthetic and orthotic devices, including impedance control, admittance control, finite-state machine architectures, and model predictive approaches
• Translate biomechanical models of human joint function, including torque–angle–velocity envelopes, inter-joint coupling, and gait phase dynamics, into control requirements and implementable controller structures
• Integrate AI/ML components into control pipelines: apply classification networks for activity and terrain recognition, recurrent models for gait phase prediction, and data-driven approaches for parameter adaptation
• Develop and apply state estimation methods, including Kalman filter variants and sensor fusion algorithms, to estimate joint states, user intent, and environmental context from on-device sensor data (IMU, force/torque, EMG, encoders)
• Design and execute experimental validation protocols on physical hardware, including benchtop testing and human subject studies in collaboration with clinical and biomechanics partners
• Collaborate with – amongst others - biomechanists and clinical partners to define functionally meaningful control objectives, distinguishing between normalisation of gait patterns and user-specific optimisation targets
• Evaluate sensory feedback modalities (force, vibrotactile, proprioceptive substitution) and assess their role in closing the human–device control loop
• Document control architectures, experimental results, and validation outcomes in technical reports
• Conduct structured literature reviews and competitive benchmarking to maintain awareness of the state of the art in prosthetic and orthotic control, wearable robotics, and human locomotion science
• Present findings to internal stakeholders, research partners, and where relevant to clinical audiences
• Engage with regulatory and product development teams to ensure control designs are compatible with applicable safety standards (EU MDR, IEC -1, ISO )

Qualifications

• MSc or PhD in Control Engineering, Biomedical Engineering, Mechatronics, Robotics, or a closely related field
• Minimum 2 and up to 5 years of verifiable experience in prosthetics, orthotics, exoskeletons, other wearable robotics, or rehabilitation robotics
• Demonstrated track record, through industry deliverables, device outputs, or publications of developing control strategies directly and verifiably informed by human biomechanical analysis
• Solid understanding of human musculoskeletal mechanics: joint kinematics and kinetics across lower and upper limb, muscle–tendon dynamics, and inter-segmental force and torque relationships
• Demonstrated experience designing and implementing real-time control systems for physical mechatronic devices with strict timing, safety, and robustness requirements
• Working proficiency in classical control methods as applied to wearable or rehabilitation robots: finite-state impedance/admittance control, PID-based architectures, and torque/position control in actuated joints
• Practical experience with state estimation for nonlinear systems (Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), or equivalent Bayesian estimator) applied to real sensor data from physical hardware
• Experience applying AI/ML methods in a control or estimation context: activity or terrain classification using IMU or force data, gait phase prediction, or parameter adaptation via data-driven models
• Proficiency with Python-based libraries for Control and ML frameworks (PyTorch, TensorFlow, scikit-learn, or equivalent)
• Strong analytical and problem-solving skills; able to structure complex biomechanical and control problems into testable hypotheses and measurable design criteria
• Collaborative by default; comfortable working in an interdisciplinary team where decisions require integration of engineering, clinical, and regulatory perspectives
• Clear and precise communicator in English, both in writing and verbally – German is a plus
• Self-directed and organised; able to manage parallel workstreams and deliver experimental results within agreed timelines

Benefits

• Competitive compensation package
• days of vacation
• Flexibility: Flexible work hours and the opportunity for remote work
• Retirement benefits: Company pension plan, capital-forming benefits
• Health and well-being: EGYM Wellpass, company sports (e.g., yoga, volleyball, tennis), external counseling for personal and professional matters
• Mobility: Carpooling platform, access to the company vehicle pool for business trips, bicycle leasing (JobRad & Bikeleasing)
• Discounts & perks: Corporate benefits and offers from local partners
• Onboarding & professional development: Personalized onboarding during the Ottobock Welcome Days & training opportunities at the Ottobock Academy