Fully-funded PhD positions in AI for Science
il y a 2 semaines
25 Nov 2023
Job Information
Organisation/Company
CNRS
Department
Groupe de recherche en Informatique, Image, Automatique et Instrumentation de Caen
Research Field
Engineering
Computer science
Mathematics
Researcher Profile
First Stage Researcher (R1)
Country
France
Application Deadline
15 Dec 2023 – 23:59 (UTC)
Type of Contract
Temporary
Job Status
Full-time
Hours Per Week
35
Offer Starting Date
1 Feb 2024
Is the job funded through the EU Research Framework Programme?
Not funded by an EU programme
Is the Job related to staff position within a Research Infrastructure?
No
Offer Description
The PhD candidate will be associated with two public research laboratories, both located in Caen. The first, GREYC ), is a computer science laboratory. The second, LPC ), is a Nuclear and Particle Physics laboratory. The Normandy region in France is known for its historical and cultural significance, offering a blend of medieval charm and modern amenities. Caen, situated in the heart of Normandy, features notable landmarks such as Caen Castle and the Abbey of Saint-Étienne. In recent years, the region has gained recognition as a hub for research and innovation, particularly in the fields of artificial intelligence (AI) and physics. With well-equipped research facilities and collaborative academic environments, Normandy provides an environment conducive to interdisciplinary studies. Additionally, the region offers a good quality of life, including cultural attractions, scenic landscapes, and proximity to the coast. These factors make Normandy, and specifically Caen, an appealing choice for prospective PhD candidates interested in AI and physics research.
Empowering Physics: Understanding Generative Models for Enhanced Data Generation
The proposed PhD research aims to conduct an in-depth investigation into the knowledge incorporation mechanisms of deep learning-based generative models, focusing on models utilizing Denoising Probabilistic Diffusion Processes (DDPM). Recent advances in generative models, exemplified by noteworthy examples such as DALL-E and Stable Diffusion, have showcased remarkable capabilities in generating diverse data, spanning from textual content to realistic images and simulations of physical processes. The central research question revolves around unraveling the intricate mechanisms underlying what knowledge these models encapsulate, the precision with which they do so, and the forms in which this knowledge is represented.
Understanding how these neural network-based methods capture the training data models is crucial for various reasons, including assessing whether the models merely store and interpolate data during generation or if they accurately capture the underlying factors governing the generation process. This investigation encompasses aspects such as generalization, memorization, stability, and multimodal generation. The complexity is further increased by the fact that DDPMs have demonstrated the capacity for both memorization and generalization, depending on the specificity of the training examples.
The research also delves into characterizing these generative models through the lens of uncertainty quantification (UQ). The goal is to construct prediction sets or intervals that provide a plausible range of values for the true outcome. Various UQ methods are explored, including model-agnostic uncertain estimates in machine learning and prediction intervals in regression tasks. Ensuring theoretical and empirical coverage guarantees for these methods is a key challenge, with the notable exception of conformalized quantile regression in the existing literature.
The overarching motivation driving this research is to enable the application of generative models, especially DDPMs, in scientific discovery, with a specific focus on the field of particle physics. In particle physics, Monte Carlo simulations are integral in providing expectations for experimental results. However, the ever-increasing complexity and volume of data in modern experiments necessitate more simulated data, making simulation tasks computationally burdensome and labor-intensive. Generative models, particularly DDPMs, are positioned as promising solutions due to their ability to generate high-resolution samples and to cover multimodal distribution with a wide range of modes. The proposal underscores the potential of these models to augment Monte Carlo datasets, expedite reconstruction processes, and enhance analyses, particularly in experiments like KM3NeT with high discovery potential. The successful candidate will have the opportunity to work with the KM3NeT research team, using real physics data, contributing directly to advancing the use of machine learning techniques in a big international science collaboration.
Candidate applications must encompass: documentation demonstrating compliance with obligatory criteria, a Master's degree certificate, a letter of motivation outlining scientific interests and plans concisely, a curriculum vitae highlighting the candidate's strengths and experience, and contact details for at least one mentor or scientist who can provide a recommendation upon request.
Requirements
Research Field
Engineering
Education Level
PhD or equivalent
Research Field
Computer science
Education Level
PhD or equivalent
Research Field
Mathematics
Education Level
PhD or equivalent
Languages
FRENCH
Level
Basic
Research Field
Engineering
Years of Research Experience
None
Research Field
Computer science
Years of Research Experience
None
Research Field
Mathematics
Years of Research Experience
None
Additional Information
Website for additional job details
Work Location(s)
Number of offers available
1
Company/Institute
Groupe de recherche en Informatique, Image, Automatique et Instrumentation de Caen
Country
France
City
CAEN
Where to apply
Website
Contact
City
CAEN
Website
STATUS: EXPIRED
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