Internship in near-sensor deep learning on parallel architectures applied to computer vision problems at the E3DA Research Unit


The Foundation is looking for a proactive and dynamic young candidate, with good organizational, relational skills and an inclination for team-work, strongly motivated to work on innovative projects featuring strong technological components (and which are frequently addressed to third party companies).

We can offer an important training opportunity to deepen and experiment in the field of embedded programming applied to low-power parallel computing architectures, as GAP8. The candidate will be implementing a Neural Network model for object detection in embedded C. After the implementation, an experimental characterization of power consumption and execution time will be performed. The final goal of the internship is a demo of the overall system running real time with a DCMI camera and LCD display.

Requirements:
  • Ongoing Academic background in Engineering or related fields (e.g. Physics, Math);
  • Knowledge of Embedded C, microcontroller programming;
  • Knowledge of Neural Networks
  • Optional, but appreciated knowledge about EfficientNet and MobileNet architectures;
  • Good knowledge of written and spoken English;

We offer:
  • Curricular internship;
  • 3-6 months, depending on candidate’s needs and preparation;
  • Canteen
  • Support for the search for accommodation at the affiliated structures;

All interested parties are requested to fill in the online form, clicking on "Apply online", and attach the required documents in Pdf format:
  • Curriculum Vitae;
  • Cover letter;
  • Reference letter (optional).

Applications will be reviewed as they arrive. Preferred period: before summer 2021.

For any information, contact Elisabetta Farella (efarella@fbk.eu).

For any support, further clarification and/or information, the Human Resources Department will remain available via the address: jobs@fbk.eu
 
Business units
Centro Digital Society/E3DA
Locations
Science and Technology Hub - Trento
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