Funded PhD Position – Biomechanics of the ocular lens
Location: Paul Donaldson Lab
We are looking for a PhD candidate for a bioengineering project hosted by School of Medical Sciences. The topic is “Ocular lens stiffness with aging and incidence of Presbyopia”. This is a fully funded project by a HRC Programme grant. The candidate will be trained at Molecular Vision Lab, based in Grafton Campus. The supervisory team would be Professor Donaldson, Dr Vaghefi and Dr Chen.
Presbyopia is an age-related eye condition when the ocular lens loses its ability to focus. In adults, it is manifested as loss of near-vision and inability in performing certain near-distance precision tasks. Currently there are no medicinal treatments for presbyopia, because the mechanism of loss of lenticular accommodation is not well understood. We hypothesize that this loss of accommodation is due to increasing stiffness in the lens, due to age related accumulative oxidative damage to this tissue. To test this hypothesis, we have created an arsenal of lens specific imaging modalities at Molecular Vision Lab. In this project, human, bovine and rodent lenses will be obtained and used to study the correlation of oxidative damage (i.e. aging) and increased stiffness, using various imaging modalities and computational modelling.
Applicants should have an background in biomedical science or bioengineering. They should be interested in laboratory work which will include tissue dissection, preparation and imaging, as well as data analysis and computational modelling. The project is set to start before March 2021
Dr Ehsan Vaghefi
Machine Learning Engineer
StretchSense – Sensor Holdings Limited
Experience: 3-5+ years or more industry experience required
Location: The role is based in Penrose, Auckland.
Help us make the best motion capture gloves ever.
The natural way to interact with augmented and virtual reality will be with your hands. StretchSense is developing incredible gloves based on soft and stretchy sensor technology for the future of human machine interaction. Our first product is a motion capture glove specifically designed for animators that need perfect finger data for movies and games.
A critical feature of our MoCap Pro product is our proprietary hand mocap software, Hand Engine. Our Windows-based software is a standalone application for hand mocap using our gloves, and it enables this data to be streamed in real time into a range of motion capture and animation software environments (e.g. MotionBuilder, Unity, Unreal) for combination with other body and face mocap sources in a production environment.
We are looking for a machine learning engineer with experience in biomechanics and 3D body pose estimation to help us push the boundaries of what our MoCap Pro Gloves and Hand Engine software can do. High performance and low latency is essential for hand motion capture in a production motion capture studio environment. You will be analysing raw data coming from reliable, repeatable stretch sensor technology in our gloves and developing machine learning algorithms to drive a 3D hand model with a precise estimation of the wearer’s hand pose in real-time. You will be using a combination of classification and regression methods, and supervised and semi/unsupervised learning to quickly identify and adapt the output of the glove to an individual’s hand for a wide range of users whilst minimizing calibration time. You will be developing, testing and supporting pose libraries, reference databases, and calibration routines and relating these datasets to kinematic/biomechanical models of the hand and using model order reduction to improve the stability and performance of the gloves across a broad population. You will also be developing a benchmarking process that will enable us to test and compare different methods and configurations in order to build and validate better models for use in real-time hand capture.
You will be working in a multi-disciplinary team of software and firmware developers that are responsible for Hand Engine and interfacing with our glove hardware, controlling, configuring and calibrating the glove output and streaming fully solved hand data to downstream animation software packages such as MotionBuilder, Unity, Unreal, Maya and more. Your work will also be an important input into the design and optimisation of future versions of our MoCap Pro gloves.
StretchSense recently came under new ownership and we are looking to expand our team. We have a collaborative culture and a multidisciplinary group of engineers and creatives. We have some of the coolest customers in the world making amazing content and products. This is a rare opportunity to join a well-funded start-up and contribute on the ground floor to building the user interface of the future.