AI engineer position
The Computer Vision Center (CVC) is a non-profit research center established in 1995 by the Generalitat de Catalunya and the Universitat Autònoma de Barcelona (UAB). Its mission is to carry out cutting-edge research that has the highest international impact in the field of computer vision. It also promotes the transference of knowledge to industry and society.
Computer vision is an exciting research area and an omnipresent technology, essentially empowering machines with the sense of vision. The CVC is a successful marriage between knowledge and innovation. In addition to our cutting-edge scientific achievements, we have established lasting ties with industrial partners and created several spin-off companies.
Research area or group: Interactive Augmented Modelling
Description of group/project:
Our research is focused on developing mathematical tools, interactive and augmented reality visualization environments. The main application field is biomedical image processing.
The group is a multidisciplinary team (including mathematicians and computer science engineers) specialized in the treatment and analysis of biomedical images. They use graphics techniques and mathematical models for the visualization and modelling of anatomic and biological structures for a better clinical diagnosis. Modelling includes extraction of, both, the geometry (contour segmentation and shape description) and dynamics (tracking of the movement or deformation) of the organ or cell under study. Visualization focuses on the development of virtual environments providing efficient manipulation of the computational model at local and remote (via internet) level.
Context and Mission:
The Interactive Augmented Modelling research group (http://iam.cvc.uab.es) from the Computer Vision Center (www.cvc.uab.es) looks for an engineer specialized in artificial intelligence methods to be incorporated in the Up4Health project funded by the Spanish Government.
The goal of the project is to develop multi-view predictive methods able to model clinical data uncertainty dynamically collected from user interactions and multimodal sensors and scans. Uncertainty in data (coming from either manual annotations or scan parameters) will be included in classifiers’ training by optimizing several measures of feature variability along with the classification loss in a multi-objective approach implemented in a deep learning architecture with multi-task learning.
Methods will be applied to two medical application scenarios:
- Lung Cancer Personalized Early Diagnosis (LuCaD).
- Driving Performance evaluation of patients with Neurodegenerative Diseases (NeuroDrive).
The candidate will help in the deployment and development of LuCaD.
- Education :
- degree in Mathematics, Physics or Engineering
- master in computer vision, mathematical modelling for engineers, data science or similar
- Essential Knowledge and Professional Experience
- Deep Learning libraries TensorFlow, Keras
- Fluency in English
- The candidate must be an effective communicator, multitask, and work well on collaborative and interdisciplinary designs.
- Ability to think creatively.
- Ability to work independently and make decisions.
- Ability to take initiative, prioritize and work under set deadlines and pressure.
- The position will be located at Computer Vision Center (Campus Universitat Autònoma de Barcelona)
- We offer a full-time contract, a good environment, flexible working hours
- Duration: until end of year 2021
- Salary: 16.074,00 euro gross per year
- Starting date: ASAP
Applicants must submit their curriculum vitae through the application online form, indicating offer code: 25062021_AI_engineer
- Pre-selection: determination of compliance with the minimum requirements of the offer.
- Selection: assessment of the preselected candidates by scoring based on objective criteria.
- Potential candidates will be contacted to set up an interview.
Application Deadline: 05/07/2021
“This project has received funding from the Ministerio de Ciencia, Innovación y Universidades (MCIU), Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). Project reference: RTI2018-095209-B-C21″