Postdoc for antenna design using machine learning

We are looking for a researcher with a PhD in Electromagnetics (EM) and Antenna design theory, with experience in advanced simulation and modeling tools, preferably using machine learning. This researcher will join our team to develop a medical device antenna that can wirelessly transmit data and harvest RF energy. The prospect candidate will join ONiO full-time through the SME innovation Associate program (INNOSUP-02-2019-2020) and thus recruitment is conditional to the award of this grant. The fellowship is a 12 months employment with a competitive salary – starting Q3 2020 – and a strong candidate will likely remain at ONiO afterward to spearhead future R&D on antenna designs. The program includes core EU training on innovation and entrepreneurial skills, as well as inhouse training on our proprietary knowledge.

At the time of recruitment, applicants must have not resided or carried out his/her main activity (work, studies, etc.) for more than 12 months over the last 3 years in Norway.

Responsibilities

ONiO is a medical sensor company founded in 2016 and we are currently developing the first non-invasive, wireless and self-powered multi-sensor platform for medical devices. We aim to expand our team by recruiting a highly motivated researcher to develop compact, low-profile antenna designs, suitable for wearable on-body applications, to be integrated with other custom components under development at ONiO. Within a 12-month timeframe, we are looking for a researcher to:

  • Research architectures, materials, and implementations for small multiband antennas for medical use, operating on or close to human skin
  • Develop at least 9 antenna design candidates (where at least 3 are active) and validate them through simulations and prototyping
  • Evaluate chosen design candidates in collaboration with other ONiO team members, implementing any revealed improvements and revalidating
  • Implement designs in a sensor system in collaboration with the team and supervise final external validation
  • Compile research documentation in line with ISO13485 regulatory requirements to facilitate the achievement of regulatory approval/clearance post-project
 
Requirements

To accomplish these goals, we are looking for a candidate with a PhD in Electronic Engineering, Antenna design theory, RF energy harvesting systems or related fields and at least 4 years of relevant experience. We value skills and proven experience in as many of the following areas:

  • Development, testing and evaluation of fractal, miniaturized and/or low-profile antenna designs
  • Development and production of electronic components for far-field multiband RF energy harvesting and/or for ultrawide band radio transmissions (antennas and other RF designs), including the use of new materials and EM/RF properties to achieve harvesting
  • Use of advanced simulation and modeling tools – particularly machine learning models – for antenna engineering design, evaluation and optimizations
  • Statistical analysis of research results for academic publication purposes
  • Industrial experience with medical devices, of which wearable on-body devices are a major plus
  • Carrying out research individually or as a team member
     

About

We will provide the candidate with access to proprietary knowledge and tailored training, as well as international R&D cooperation with EU partner entities. These partners will enrich the diversity of the work plan and ensure high-quality academic outcomes. The chosen candidate will report directly to the CTO and collaborate closely with our R&D team. While this project is limited to 12 months, there is a definite opportunity for long-term employment at ONiO following the project. This project will be funded by the SME Innovation Associate Programme (INNOSUP-02-2019-2020), covering competitive salary rates. The candidate is expected to relocate to Trondheim, Norway for the duration of the project. The relocation cost of the researcher will be covered by ONiO and the EU.

Postdoc for antenna design using machine learning
ONiO
Trondheim
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