TEAM
Our management team of industry experts in business, technology and research have a strong track record in delivering complex solutions. We have grown to a total of 17 employees in total.
CARSTEN BRINKSCHULTE
CEO, Management, Technical and Corporate Strategy, Marketing & Sales
Serial entrepreneur (3 exits) with 20 years experience in mobile network infrastructure. Previously Core Network Dynamics (Twilio), Movirtu (Blackberry), Synchronica.
DANIEL
HOLLOS
Embedded Software Lead
Senior Embedded Systems and Mesh Network Software Engineer with 12+ years’ experience in embedded system design and development.
MARCO BÖNIG
Hardware Development Lead
Expert in RF hardware and custom design of electronic solutions, holds a patent for energy harvesting for smart home products.
BEN
BANERJEE
SVP, Worldwide Sales
Experienced business development leader, professional services and telecom background. Previously Infosys, Synchronica, Microsoft, Telus, Bell Canada.
CHERIAN MATHEW
Cloud and Analytics Software Lead
16+ years of experience in software architecture, design and development in both industry and academia with a focus on cloud based data analysis systems.
DR. HABIL. JÜRGEN MÜLLER
Research and Scientific Advice, Strategic Partnerships
Until recently leader of department of forest ecology Thünen-Institute of Forest Ecosystems. Developed INPRIWA, a prototype for early forest fire detection.
INVESTORS
Our investors are a combination of financial, strategic and impact investors.
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JOB OPENINGS
As a growing company, we are always looking for new team members. Whether you’re interested in business or have something innovative and unique to bring to the table, we’d love to see if you’re a good fit for Dryad. Send us your CV today to and we will get back to you as soon as we can.
Embedded AI Engineer
Eberswalde / Berlin
Contributing to smoke test experiments with regards to understanding and organization of resulting experiment data.
Experiment with sensor deployments on-site, also in forests.
Development of ETL pipeline to streamline the experiment data to AI models process.
Development of a smoke recognition model based on outputs of several sensor readings; ML experience including deep learning is required
Development of the model training system deployed in the cloud, and eventually, on resource constrained embedded devices
Development of the operation for running the smoke detection model running on the resource constrained
Development of smoke detection models for ultra low power LoraWAN sensor node software both for arm microcontrollers, both on the sensors as well as on the cloud.