Vil du jobbe med stordata og maskinlæring for fremtidens landbruksroboter, i fjellsport eldoradoet Sogn?

Company:

Fieldwork Robotics (FWR) develops modular, multi-crop, soft and adaptive robots able to operate alongside people in the agricultural environment on tasks such as selective harvesting of fruits and vegetables.

The company has its main offices in Cambridge, UK, has continuous field-testing activities in Odemira, Portugal, and has a growing development hub for new technologies and services in scenic Sogndal, Norway. We provide leading edge solutions based on innovative technologies, in a fast-paced environment, and work with leading RD&I institutions.

Our technology includes advanced machine learning for assessing crop maturity and other crop features, as part of the robot control loop, but we also exploit this data for valuable data services for growers. 

Job Summary:

We are seeking to employ a talented, enthusiastic and suitably qualified Data Scientist to work on active projects with autonomous and data-driven robotic platforms for horticulture. 

You will be tasked with the development of machine learning approaches and data pipelines, and their utilisation for novel data services for growers. This will include data gathering, fusion and processing, the integration of edge/cloud processing with our robotic platform, and data visualisation/exploitation.

This represents a fantastic opportunity for a skilled professional to join one of the most exciting brands in the robotics market. As a high-growth company, roles do change and evolve, so you will be able to expand on your key duties within the role to help the team deliver their objectives. 

The position is for a permanent contract. You must be eligible to work and live in Norway. The position requires some national and international travel.

Responsibilities include the following:

• Lead the development of new data services for growers based on the data from the FWR robots, in combination with other data sources.
• Work on the training and testing of deep learning algorithms for the entire machine learning life-cycle, from data collection, to labelling and final algorithm integration in the FWR platform.
• Work with our mechanical and electronic teams to ensure optimised integration with other system components, and the manufacturing process
• Write documentation for requirements, and write well tested and documented code to meet those requirements
• Work with external partners, end-users, stakeholders, and collaborators.
• Work at all stages of the system development cycle from concept and requirements capture through to transfer to production.

Qualifications/Experience:

The successful candidate will have most of the following:

• Masters Degree in data science, machine learning, computer science or equivalent (lower degrees considered in exceptional cases) 
• At least 4 years of work experience with data science and machine learning (in particular related to image datasets)
• Previous experience with data versioning, and creating and maintaining training, test, and validation datasets used for machine learning models
• Proficiency with deep neural networks, and in using common deep learning frameworks (such as PyTorch or TensorfFow)
• Some experience with cloud compute providers (e.g. Google Cloud, AWS, Azure). Specifically, deploying and maintaining automatic data processing pipelines.
• Good problem solving, analytical and organisational skills, and attention to detail.
• Good team-working ethics, but able to work on own initiative with minimum supervision.

Experience with some or all of the following is beneficial: 

• Common big data frameworks (Apache Hadoop, or similar)
• Data fusion and data interoperability
• Big data visualisation techniques and tools
• RGB-D cameras, OpenCV, object tracking
• Embedded hardware for machine learning applications
• Agricultural robots

Benefits:

• Share options
• International travel
• Company events & social hours
• Casual dress
• Flexible working hours
• Possibility to work remotely
• Great location for all-year mountain sports