We are always looking for motivated Ph.D. students, post-docs, and Master's students to work with us. If you're interested in conducting high-quality research in one of these themes (or close), please reach out to us.
We are currently hiring two (2) M2 research interns (with Ph.D. funding available) for the PEPR TwinFarms project! (see below)
The Ekinocs team at Inrae and the DIVA team at Télécom Paris are recruiting for two M2 internships in visualization and HCI as part of a PEPR AgroEcoNum project (TwinFarms) for deploying digital twins at farm scale to promote agro-ecological innovation.
Context
The concept of digital twins (DTs), derived from engineering, has enabled improvements in many areas including for physical and chemical processes. In DT, there is a coexistence of a physical object and a digital object. The digital object is presented as a “copy” of the real object, hence the term “twin”.
With the increasing number of sensors in farms, the amount of data collected, combined with already acquired knowledge, could support the agroecological transition in the face of climate change. Although DT’s for agriculture are rare (for a review of digital twins in agriculture, see [6)], biological and social systems are beginning to see the birth of their digital twin.
The goal of the TwinFarm project is to empower farmers to make informed decisions by simulating outcomes in a virtual environment, thereby potentially reducing real-world risks and encouraging more experimental and innovative farming strategies.
The expected contributions of this digital twin are thus two fold: (1) to optimize management decisions of the real twin in real time based on the information collected by the sensors located there; and (2) to carry out digital experiments to test the consequences of modifications before implementing them in a real system.
Scope and objectives of the internships
Visualisation plays a central role in digital twins by providing a graphical user interface to represent the real-world system and its digital counterpart. Although there is a lack of a systematic study of visual analytic techniques for digital twins, recent examples demonstrate how visualisation can be used for model output correction, state monitoring and display, equipment simulation and prediction, and more generally for human-machine interaction and collaboration [2]. Despite the promise of DTs, they must overcome two main barriers: (i) techniques need to present the right data in an intuitive and insightful manner for users in contextually relevant ways; (ii) uncertainty is inherent in all data, arising for example from measurement noise and errors, modeling errors and approximations, etc [5]. Both of these barriers are exacerbated by the mediation of the ML models between raw data and final representation.
Following a user-centered design methodology, the recruited students will design, implement and evaluate an interactive user interface for ML results visualisations, to assist operators in quickly and reliably monitoring the achievable goals (given the current process state), providing timely opportunities to change the course of action.
Internship 1 aims to develop methods for presenting the digital twin in an intuitive and insightful way, with a focus on interactive exploration [4] and user-customizable systems [1] that enable domain-expert non-programmers to tailor data representations to their specific needs.
Internship 2 focuses on uncertainty communication and monitoring user trust [3]. The goal is to propose and validate effective methods to communicate the intrinsic uncertainties that arise throughout the data visualisation pipeline, from sensor to visualisation.
Student profile
Required skills include:
- web development;
- programming (JavaScript/D3.js, Python, other)
- Interest in working with real-world data and domain experts.
Additional skills:
- Knowledge in machine learning is not required but experience is a plus
- Experience with user-centered design & information visualization is a plus.
Funding is available for two PhD students following these internships.
Work environment
- Supervisors: Nadia Boukhelifa and James Eagan
- Internship duration: 4–6 months, ideally starting in March or soon thereafter
- Location: Palaiseau (AgroParisTech Saclay Campus and Télécom Paris, about a 5-minute walk apart)
- Stipend: around 650 euros per month
To Apply
Send CV & cover letter to nadia.boukhelifa@inrae.fr and james.eagan@telecom-paris.fr
References
[1] Badam S.K. et al. (2019). Vistrates: A Component Model for Ubiquitous Analytics. IEEE TVCG, 25(1). [2] Botin-Sanabria D.M. et al. (2022). Digital Twin Technology Challenges and Applications: A Comprehensive Review, Remote Sensing, 14(6), 1335. [3] Boukhelifa, N., Perrin, M.-E., Huron, S. and J. Eagan (2017) How data workers cope with uncertainty: A task characterisation study. In CHI ’17: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, CHI ’17, 3645–3656, New York, NY, USA. ACM. [4] Boukhelifa, Nadia, et al. "An exploratory study on visual exploration of model simulations by multiple types of experts." Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. [5] Van der Horn E et al. (2021). Digital Twin: Generalization, characterization and implementation. Decision Support Systems, 145, 113524. [6] Verdouw, C., Tekinerdogan, B., Beulens, A., Wolfert, S. (2021) Digital twins in smart farming.Agric. Syst., 189, 103046.