LFG!

Koniku is an early-stage company based in San Rafael, CA. We are about 30 minutes outside San Francisco. We build cyborgs by merging genetically modified living biological neurons with silicon or electronics, optics, mechatronics and other technologies. We are a full-stack “wetware” technology company. That means we build & design our biochemical & transduction pathways, design DNA constructs, and modify GPCR binding affinities. We also make our cell lines, write our firmware, make our ML models and our communications modules for IoT and more. We design and build our biokores with microfluidics. We have a deep and differentiated device that seamlessly merges synthetic neurobiology with silicon into an elegant and delightful smell cyborg that solves real-life problems. It’s wetware because the biological neurons are hosted in a proprietary aqueous media with essential nutrients that promote neural longevity. Our flagship smell cyborg, the Konikore™, detects smells using GPCR’s or olfactory receptor proteins hosted in biological cells. We have applications in healthcare, security, synthetic flavors & foods, and more. It's a versatile small form factor, multi-channel smell cyborg or smell “camera”!We work with fortune 500 companies as Technology Integrators in the Koniku Technology Integrator Ecosystem (KTIE). Koniku’s investors, partners and customers include Airbus, BASF, Thermo Fisher Scientific, Fraunhofer Institute Stuttgart, Ab InBev, P&G, SoftBank, Platform Capital, IndieBio (SOSv) and others. We aim to have a Konikore™ in 10 million homes within this decade. We are driven by a fundamental belief that “Bio is Tech™”. Koniku is a face to face company; whatever the future may bring, we are here for it. It's a call for more science, not less. We are a deep technology company at Koniku; we solve hard problems which combine many disciplines. We seek elegant solutions and admire simple solutions.

• Wavefront shaping in multiphoton microscopy.• Design, build and use of control systems for multiphoton microscopy.• In vitro imaging of visual thalamocortical slices.• Deep tissue/cortical imaging.• Cortical blood flow dynamics/blood vessel imaging e.g. Alzheimer's models.• Automated or machine patching of neurons guided by fluorescence multiphoton microscopy.

• Modelling of co-cultures of spinal cord neurons, myofibrils and dorsal root ganglia (sensory) neurons on-chip. • Surface functionalization of active metal electrodes for neuron recording using APTES, collagen, elastin, and laminin constructs. • Recreating the reflex arc on-chip. • Recreating the spinal half centres ex-incubator using live stimulation protocols lasting over several weeks in self-built incubators. • Developing electrical and chemical stimulation protocol for connected neuron re-aggregates on-chip. • Modelling electrical transport or propagation of action potentials in self-designed channels. That is, injecting noise to disrupt actional potential propagation myelinated and unmyelinated axons/neurites on-chip. • Building external closed-loop live, life support systems for in vitro cultures or systems. • Modelling real/living biological neural networks in vivo and vitro.

Lead the design of data, power and biocompatibility requirements for an implantable peripheral neurochip.Initiate and maintain collaboration with key players in the academia. Update board of directors through meetings and quarterly reports. Communicate and implement board resolutions in the academia. Additionally, analyse and project market trends with respect to the neuroprothesis landscape.

Programming custom user scenario's. That is, visit factories and assess user profile, technical requirements, workflow and develop a solution where a pick and place robot replaces a human in a human centric workflow.Monitor and visit users across Switzerland ensuring the assumptions made are sane and update the system.Assemble hardware components for robot arms leaving the lab. Implement endurance and precision tests for the robot arms.In collaboration with colleagues implemented learning algorithms (statistical learning) for robot !arms in object recognition for real world applications.