Deggendorf Institute of Technology | Researcher/PhD student | REMOTE | Germany | Part-time possible
I am seeking a researcher/PhD student to investigate energy-efficient and fault-tolerant machine learning (ML) hardware as part of a research project supported by a grant. As an ML hardware researcher you will collaborate with me on novel research & development related to energy-efficient training of reinforcement learning workloads, fault-tolerance of ML hardware and impact of emerging hardware architectures on ML. Your work will be crucial for improving the carbon footprint and reliability of the future of AI and its impacts on society.
Your day-to-day activities will be researching the latest developments in the field, discussing new ideas for improvement, evaluating these ideas by conducting experiments on hardware and publishing your findings. Over the course of a year, I anticipate you will have produced 1-2 leading papers. You do not have any teaching duties.
An ideal candidate would have experience in implementing reinforcement learning from scratch on an FPGA, intellectual curiosity and be skilled at communicating technical topics clearly. A Masters degree is necessary. You are (also) encouraged to apply if you have related experience and interest in this field, e.g., you only worked with FPGAs, but do not have any experience with machine learning.
I am seeking a researcher/PhD student to investigate energy-efficient and fault-tolerant machine learning (ML) hardware as part of a research project supported by a grant. As an ML hardware researcher you will collaborate with me on novel research & development related to energy-efficient training of reinforcement learning workloads, fault-tolerance of ML hardware and impact of emerging hardware architectures on ML. Your work will be crucial for improving the carbon footprint and reliability of the future of AI and its impacts on society.
Your day-to-day activities will be researching the latest developments in the field, discussing new ideas for improvement, evaluating these ideas by conducting experiments on hardware and publishing your findings. Over the course of a year, I anticipate you will have produced 1-2 leading papers. You do not have any teaching duties.
An ideal candidate would have experience in implementing reinforcement learning from scratch on an FPGA, intellectual curiosity and be skilled at communicating technical topics clearly. A Masters degree is necessary. You are (also) encouraged to apply if you have related experience and interest in this field, e.g., you only worked with FPGAs, but do not have any experience with machine learning.
More info: https://aydos.de/looking-for-a-researcher-phd-student-in-ene...