Position: Research Assistant in Robot Learning
Category: An initial one-year employment contract will be offered with the possibility for renewal depending on performance and project needs.
Location: Nicosia, Cyprus
Preferred Start Date: September 2023
Application Deadline: The position will remain open until filled
No. Of Positions: 1
Salary Range: €18,000 - €28,000 (depending on the applicant qualifications and experience)
About the position
We seek a highly motivated and skilled Research Assistant to join the Learning Agents and Robots research group at the CYENS Centre of Excellence. As part of our team, you will conduct original research on robot learning, including, but not limited to, training robots in simulation using deep reinforcement learning, and conduct experiments on physical robots.
The Learning Agents and Robots (LEAR) research group conducts research on artificial intelligence and its intersection with robotics, focusing on creating software agents and robots that learn to predict and understand the world, solve tasks, accumulate skills, interact with humans or other agents, and make adaptive decisions in complex, dynamic and uncertain environments. To achieve these goals, the group investigates approaches from machine learning (e.g., deep learning, reinforcement learning, continual learning), evolutionary computation (e.g., quality-diversity optimization), robotic simulation, perception, planning and control, among others. Our robotics lab is currently populated with a Unitree A1 quadruped robot, a TurtleBot 4 mobile robot, and 4 Parrot Anafi drones.
The successful candidate will work under the supervision of the LEAR team leader, Dr. Vassilis Vassiliades.
Responsibilities of Research Assistant:
General qualifications and requirements
- Research, design and implement robot learning methods.
- Publish original research.
- Disseminate research findings at conferences and events.
- Collaborate with other team members, teams or external researchers.
- Participate in innovation activities related to the exploitation of research outputs.
- Attend technical and non-technical training courses and workshops as required.
- Undertake any other required duties based on team’s needs.
Profile of the ideal candidate:
- Bachelor's degree and postgraduate degree in a relevant field (e.g., Computer Science/Engineering, Electrical Engineering) from an accredited institution.
- Excellent programming skills in Python and/or C++.
- Experience with Machine/Deep Learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch, JAX).
- Experience with robotic simulators (e.g., Gazebo, PyBullet, Isaac Sim), physics engines, ROS/ROS2, or physical robots.
- Excellent written and oral English-language skills.
- For non-EU applicants a work permit will be required.
- High level of commitment, self-motivated with the ability to take initiative and work independently.
- Very good analytical skills coupled with attention to detail.
- Adaptable, hands-on and results driven.
- Excellent collaboration and interpersonal skills.
Take advantage of this opportunity for your professional and personal development by being a part of our fast-growing Research and Innovation Centre of Excellence. A very attractive remuneration package will be offered to the successful candidate according to qualifications and experience, that ranges between €18,000 - €28,000 for research assistants.
For full consideration, interested applicants should submit the following items via the: “Application for Research Assistant in Robot Learning – LEAR MRG”
Process for Research Assistants:
- A cover letter which clearly specifies 1) academic and research experience, 2) any relevant industry experience, 3) rationale on why you think you would be a good fit for the position, 4) contact details, and 5) employment availability date.
- A detailed curriculum vitae in English.
- Copies of academic degrees (BSc/MSc) and transcripts.
- Contact details of two University professors or one University professor and one industry referee who will provide the letters.
Incomplete applications will not be accepted.
In case you previously applied for a post at CYENS CoE, a new application is required.
For general enquiries, applicants may contact the HR Department of CYENS, Centre of Excellence at firstname.lastname@example.org
About CYENS CoE
Centre of Excellence is focusing on Interactive media, Smart systems and Emerging technologies aiming to empower knowledge and technology transfer in Cyprus and wider region. It is a joint venture between the three public universities of Cyprus: the University of Cyprus, the Cyprus University of Technology, and the Open University of Cyprus, the Municipality of Nicosia, and two renowned international partners, the Max Planck Institute for Informatics, Germany, and, the University College London, United Kingdom. This project has received funding from the European Union’s Horizon 2020
research and innovation programme H2020-WIDESPREAD-01-2016-2017 (Teaming Phase 2) under grant agreement No. 739578, as well as from the Cypriot Government, local and international partners, and other sponsors.
Research in CYENS integrates the Visual Sciences, Human Factors & Design, and Communications & Artificial Intelligence, in a tight synergy that provides a unique interdisciplinary research perspective that emphasizes an “Inspired by Humans, Designed for Humans” philosophy. The Centre conducts excellent, internationally competitive scientific research delivered by 18 high-caliber multidisciplinary research teams. CYENS engages in knowledge transfer and innovation activities aiming to act as an integrator of academic research and industrial innovation, towards the sustainable fueling of the scientific, technological, and economic growth of Cyprus and Europe.
CYENS Centre of Excellence is an equal opportunity employer and the position is open to everyone, internationally.
All applications are treated in the strictest confidence.
Location: Nicosia, Cyprus
Type: An initial one-year employment contract will be offered with the possibility for renewal depending on performance and project needs.