Post-doctoral Research Visit F/m Developing Lightweight Neural Network Architectures for Enhanced Ai in Hospital Settings

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat**: CDD

**Contrat renouvelable**: Oui

**Niveau de diplôme exigé**: Thèse ou équivalent

**Fonction**: Post-Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria centre at Université Côte d'Azur includes 42 research teams and 9 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM...), but also with the regiona economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

**Contexte et atouts du poste**:
The project aims to create novel lightweight neural network architectures specifically designed to improve the use of artificial intelligence (AI) in critical healthcare settings, such as hospitals. The ultimate goal is to enhance medical imaging and analysis capabilities, as well as adapt AI models to diverse data types like text and biological data.
- By collaborating with the Centre Antoine Lacassagne of Nice, we will gain access to clinical expertise and a wealth of heterogeneous data, facilitating the development of more accurate and robust AI solutions. The project's focus on image segmentation and domain adaptation will enable the integration of full body PET/CT imaging data with healthcare records and biological information.

**Mission confiée**:

- Our approach will involve designing and implementing lightweight neural network architectures that can efficiently process complex medical data while maintaining computational efficiency. We will draw upon a range of techniques, including transfer learning, knowledge distillation, and neural cellular automata, to create models that are not only effective but also scalable and interpretable.

Our development process will prioritize the production of high-quality analysis outputs, ensuring that our findings are actionable and provide tangible value to healthcare professionals. We will maintain an iterative approach, refining our models and techniques as needed to address emerging challenges and refine our results.

**Principales activités**:

- Conduct research and development of novel lightweight neural network architectures for medical image analysis, such as image segmentation, object detection, and image classification
- Develop and optimize algorithms for medical image processing, including image denoising, image enhancement, and image registration, using lightweight neural networks
- Collaborate with clinicians and radiologists to collect and annotate large datasets of medical images, and to validate the performance of developed models in real-world hospital settings
- Implement and test models on various hardware platforms, including GPUs, CPUs, and specialized AI accelerators, to ensure efficient deployment in hospital settings
- Develop and maintain software tools and libraries for medical image analysis, including data preprocessing, model training, and model evaluation
- Publish research findings in top-tier conferences and journals, and present results at international conferences and workshops
- Supervise and mentor graduate students and junior researchers in the development of their research projects and skills

**Compétences**:

- Strong background in statistical and deep learning
- Experience with medical image analysis, including image segmentation, object detection, and image classification
- Proficiency in programming languages such as Python
- Familiarity with deep learning frameworks such as TensorFlow, PyTorch, and Keras
- Experience with medical imaging software and libraries, including ITK, 3D Slicer, and scikit-image
- Knowledge of image processing techniques, including image denoising, image enhancement, and image registration
- Understanding of computer vision and machine learning concepts, including feature extraction, object recognition, and pattern classification
- Experience with model optimization techniques, including pruning, quantization, and knowledge distillation
- Familiarity with hardware platforms, including GPUs, CPUs, and specialized AI accelerators
- Strong understanding of data structures and algorithms, including data preprocessing, model training, and model evaluation
- Strong analytical and problem-solving skills, including the ability to design and implement experiments, and to analyze and interpret results
- Excellent communication and collaboration skills, including the