IFPEN’s doctoral thesis positions for next fall are now available!
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IFP Energies nouvelles (IFPEN) offers a PhD opportunity in the field of Non-Intrusive Load Monitoring (NILM) applied to industrial environments. The project aims to develop an innovative method using Convolutional Neural Networks (CNN) to enhance the accuracy of device energy signature detection.
Considering the sources of uncertainty in system design is essential in IFPEN applications for sustainable energy mix: e.g. to achieve the expected performance levels for an electric motor, for example, or for the reliability of a wind turbine.
Catalytic processes play a key role in many industries, and the optimization of these systems remains a major challenge. Combining the exceptional performance of homogeneous catalysis with the ease of implementation of heterogeneous catalysis is a major challenge.
Deciphering runaway mechanisms in Lithium-ion batteries by in mortem spectroscopic approaches
Currently, most industrial H₂ is derived from natural gas and oil, emitting significant CO₂. “Green H₂” represents only 1% of production and is mainly produced via water splitting using scarce and expensive noble metal catalysts like Pt, which have a high carbon footprint.
As plastic waste defines the present era, efforts towards the development of recycling technology are ramping up. For polyolefins (PO), common packaging materials, the available technologies of mechanical recycling and of pyrolysis (thermal conversion) have several drawbacks and can process only a fraction of waste.
Context: Currently, only 10-20% of end-of-life catalysts are recycled after their use in refineries. However, these catalysts contain critical metals (Ni, Co) and the management of these resources is becoming increasingly constrained and regulated. This makes catalyst recycling a major environmental and economic challenge.
Tighter regulations on the consumption of fossil fuels are currently driving the market for bio-oils derived from liquefaction or pyrolysis processes. To make them compatible with conventional fuel bases, bio-oils are stabilized via a moderate hydrodeoxygenation step involving sulfide catalysts.
Context
IFPEN aims to position itself as a provider of innovative, cleaner, and more efficient processes. The proposed PhD thesis falls within the framework of the implementation and optimization of liquid-liquid extraction for metal recovery in the context of battery recycling.
This PhD position deals with Physics Informed Machine Learning (PIML) modeling methodologies, a novel generation of deep-learning architectures that combines physical laws with data-driven approaches.