Junior Research Scientist in artificial intelligence for robotic

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INRAE presentation

The French National Research Institute for Agriculture, Food, and the Environment (INRAE) is a public research establishment under the dual authority of the Ministry of Agriculture and the Ministry of Research.

INRAE is recruiting researchers by open competition and offering permanent position.

It is a major player in research and innovation created on 1^st of January 2020. INRAE is a research institute resulting from the merger of INRA and IRSTEA. It is a community of 12,000 people with more than 200 research units and 42 experimental units located throughout France.

The institute is among the world leaders in agricultural and food sciences, in plant and animal sciences, and is 11^th in the world in ecology and environment. INRAE’s main goal is to be a key player in the transitions necessary to address major global challenges. In the face of the increase in population, climate change, scarcity of resources and decline in biodiversity, the institute develops solutions for multiperformance agriculture, high quality food and sustainable management of resources and ecosystems.

Work environment, missions and activities

TSCF research unit of INRAE Clermont-Ferrand (https://www6.ara.inrae.fr/tscf/), composed of 60 people, works on the design of new technologies and agricultural materials to implement the ecological transition of agriculture. TSCF is a member of I-SITE CAP 20-25 (https://cap2025.fr/).

TSCF is conducting research into the development of robots capable of operating in natural environments in a precise, safe and repeatable manner. In this area, around twenty TSCF agents bring together multidisciplinary skills to design perception and control algorithms, making it possible to provide robots with the adaptation capabilities necessary for carrying out agricultural work, thus supporting the popularization of the principles of agroecology. In particular, several robotic behaviors (such as trajectory or rank tracking, target tracking, tool coordination, or control of robots) have been developed within the framework of collaborative projects. They made it possible to obtain generic results, in the field of control, data fusion, human/machine interaction and operational safety, applicable to different types of robots (modes of locomotion, size, mass).

These results highlighted the need to be able to adapt or even reconfigure the behavior of mobile robots depending on the task and the context. In this context, you will conduct research in the field of Artificial Intelligence for robotics. You will design and implement high-level decision mechanisms for mobile robots. This involves the ability to select and weight the most appropriate perception and control approaches, depending on the task and context. Furthermore, the natural and agricultural environment is complex, making it difficult to select and tune deterministic control approaches. Thus, you will work on solutions allowing robots to adapt autonomously to unforeseen situations in an off-road context. Robot adaptation is not only limited to the environment, but also includes interaction with people. You will need to consider the ability of robots to interpret human actions to improve the ergonomics of robotic approaches to agriculture. You will also capitalize on previous work on the development of adaptive and predictive robotic architectures. This will involve continuing the creation of libraries of elementary behaviors for the autonomy of robots in natural environments. Deep learning techniques will be essential to modify the parameters of low-speed perception and control algorithms. You will contribute to the application of these techniques to improve the adaptability of robots, by integrating environmental recognition. Concerning the autonomous supervision of behaviors, the short-term objective will be to develop autonomous supervision tools capable of selecting and controlling basic behaviors already existing or being developed within the research team. In addition to leveraging perception data for control laws, you explore additional measures to improve performance. This can include the use of spatial data such as humidity, weather conditions, as well as the exploitation of enriched maps from previous passages or sensor networks.

You will have access to research infrastructures of TSCF and you will use hardware and software simulation tools to facilitate the creation of datasets for AI. You will collaborate with other researchers from TSCF and its partners, such as those from I-ISITE CAP 20-25. You will be fully involved in the TSCF unit project, which aims in particular to develop integrated approaches for the autonomy, agronomic and ecological performance, and safety of agricultural materials. You will thus contribute to offering tools to democratize the principles of agroecology, while limiting the workload and the arduousness induced by agricultural work.

Our research team tells you more about your future job

Training and skills

You hold a PhD degree or equivalent.
Having completed a thesis in the field of computer science or artificial intelligence is highly recommended. Having a solid understanding of artificial intelligence principles and techniques, such as machine learning, deep learning, vision computer and autonomous decision-making would be a plus.

You have shown your ability to use the programming languages commonly used in artificial intelligence and robotics.

A curious nature, a taste for teamwork, necessary in the targeted field of research, a keen practical sense and a pronounced taste for experimentation would be desirable. Significant integration skills will be necessary to get involved in current projects.
Certain dynamism is expected in order to be a force for proposals and to contribute to the construction of new projects and collaborations. An ability to communicate effectively is also expected, through scientific publications, presentations, or reports. 

Fluency in English and knowledge of French are desired, as is long-term international experience: successful candidates who have not yet gained such experience will be strongly encouraged to make a stay abroad at the end of the internship year, prepared jointly with the host team.

INRAE's life quality

By joining our teams, you benefit from:

- 30 days of annual leave + 15 days "Reduction of Working Time" (for a full time);
- parenting support: CESU childcare, leisure services;
- skills development systems: training, career advise;
- social support: advice and listening, social assistance and loans;
- holiday and leisure services: holiday vouchers, accommodation at preferential rates;
- sports and cultural activities;
- collective catering.

For international scientists: please visit your guide to facilitate your arrival and stay at INRAE.