Research Scientist in Neuromorphic AI Computing and Engineering

Conducting foundational explorations in AI efficiency and pushing the technology readiness of state-of-the-art neuromorphic research.

Researching and developing efficient/neuromorphic AI algorithms (models, advanced features, learning methodologies, etc), and participating in the creation of intellectual property (research papers in high-impact conferences/journals and patents).

Analysing and optimizing ML models running on neuromorphic (dataflow) hardware architectures to enhance energy efficiency, reduce latency, and improve performance and scalability of edge AI processing.

Supporting algorithm-hardware co-design and co-optimization by investigating architectural improvements, and algorithmic optimizations, and proposing new features for Innatera’s accelerators and sensor solutions.

Building demonstrators and prototyping end-to-end (sensor-to-actuation) AI processing pipelines and applications on edge devices and Innatera hardware.

Exploring and identifying unique selling points of neuromorphic technology in various application domains (e.g. robotics, smart spaces, communications, signal processing, bio-medical, etc).

Supporting application developers with foundational ML/AI know-how, and spedifically in aspects pertinent to energy and latency efficiency of AI.

Engaging in collaborations with our academic, business, and community project partners (capable of mentoring junior engineers/researchers).

Thriving overall in a multi-disciplinary and dynamic environment (that includes hardware designers, platform developers,algorithm researchers, and embedded application developers) with the aim to bring neuromorphic AI technology to the market.

Research-level experience in Neuromorphic computing and Engineering (PhD degree preferable). With a publication track record in a relevant area (e.g., ML/AI, neuromorphic computing/engineering). 

Solid knowledge of Deep-Learning (and ideally broader ML) theory and foundations.

Good in software conceptualization and prototyping (able to write tidy and readable code). Experience with tooling for ML/AI development/testing (Python, Pytorch/TF, ScikitLearn, etc).

Systematic in empirical experimentation, data analysis and documentation of results.

Experienced with drafting research manuscripts for publications. 

Some background in optimization (linear/non-linear).

Some knowledge of computer architectures.

Experience with writing project proposals and supervising/mentoring junior researchers/engineers.

Some proficiency in C/C++ and embedded systems programming.