Machine Learning Internship - Foundation Model Adaptation For Multiple Sclerosis
Raidium
Engineering
On-Site
Paris
Posted 6 months ago
Job Description
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
You
- Finishing MSc in Mathematics / Computer Science / Electrical Engineering;
- Proficiency with statistics / Machine Learning algorithms
- Proficiency with Deep Learning, Computer Vision algorithms and/or NLP algorithms
- Proficiency with Python
- Willingness to work in the healthcare industry
- Authorization to work in France
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
You
- Finishing MSc in Mathematics / Computer Science / Electrical Engineering;
- Proficiency with statistics / Machine Learning algorithms
- Proficiency with Deep Learning, Computer Vision algorithms and/or NLP algorithms
- Proficiency with Python
- Willingness to work in the healthcare industry
- Authorization to work in France
Pluses
- Experience in an agile work environment
- Publication history in top-tier Machine Learning or healthcare journals
- Experience in healthcare and/or with the manipulation of radiological images
- Experience with Self-Supervised Learning and Transformers
- Experience with HPC clusters and GPU-based infrastructures
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
You
- Finishing MSc in Mathematics / Computer Science / Electrical Engineering;
- Proficiency with statistics / Machine Learning algorithms
- Proficiency with Deep Learning, Computer Vision algorithms and/or NLP algorithms
- Proficiency with Python
- Willingness to work in the healthcare industry
- Authorization to work in France
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
You
- Finishing MSc in Mathematics / Computer Science / Electrical Engineering;
- Proficiency with statistics / Machine Learning algorithms
- Proficiency with Deep Learning, Computer Vision algorithms and/or NLP algorithms
- Proficiency with Python
- Willingness to work in the healthcare industry
- Authorization to work in France
Pluses
- Experience in an agile work environment
- Publication history in top-tier Machine Learning or healthcare journals
- Experience in healthcare and/or with the manipulation of radiological images
- Experience with Self-Supervised Learning and Transformers
- Experience with HPC clusters and GPU-based infrastructures
Why join us
- Have a real and positive impact on people's lives
- Apply and improve the latest Deep Learning technologies to build an inspiring product
- Be in contact with radiologists in our partner center
- Be among the first employees of Raidium
- Work with a team of internationally recognized experts
- Work with a diverse team of engineers and medical doctors
- Competitive salary
- Access to premium computing hardware
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
You
- Finishing MSc in Mathematics / Computer Science / Electrical Engineering;
- Proficiency with statistics / Machine Learning algorithms
- Proficiency with Deep Learning, Computer Vision algorithms and/or NLP algorithms
- Proficiency with Python
- Willingness to work in the healthcare industry
- Authorization to work in France
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
You
- Finishing MSc in Mathematics / Computer Science / Electrical Engineering;
- Proficiency with statistics / Machine Learning algorithms
- Proficiency with Deep Learning, Computer Vision algorithms and/or NLP algorithms
- Proficiency with Python
- Willingness to work in the healthcare industry
- Authorization to work in France
Pluses
- Experience in an agile work environment
- Publication history in top-tier Machine Learning or healthcare journals
- Experience in healthcare and/or with the manipulation of radiological images
- Experience with Self-Supervised Learning and Transformers
- Experience with HPC clusters and GPU-based infrastructures
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
You
- Finishing MSc in Mathematics / Computer Science / Electrical Engineering;
- Proficiency with statistics / Machine Learning algorithms
- Proficiency with Deep Learning, Computer Vision algorithms and/or NLP algorithms
- Proficiency with Python
- Willingness to work in the healthcare industry
- Authorization to work in France
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
About Raidium
Raidium is developing the most advanced radiological LVLM (Large Vision Language Model); The first version of our Foundation model, Curia, is now the state-of-the-art copilot of the new generation of radiological image manipulation software.
The internship - Foundation model adaptation for Multiple Sclerosis
- Multiple Sclerosis (MS) is a complex chronic neurological disease. Early and accurate diagnosis via MRI, relying on subtle, tiny lesions and specialized sequences (FLAIR, SWI), remains a significant challenge in clinical practice.
- Foundation models (like DINOv2) leveragingSelf-Supervised Learning (SSL) have revolutionized computer vision, showcasing superior generalization. While their medical adaptations like Rad-DINO and Curia (developed at Raidium, pre-trained on 200M+ medical images that come from 150K CT scans and MRIs) exist, adapting them for MS imaging faces diminishedefficacy.
- This challenge arises because the MS-defining features—specifically the tiny, sub-millimeter lesions and the data from specialized sequences (SWI-m, SWI-ph)—constitute ahighly granular and underrepresented domain within large-scale medical pretraining datasets.
- Adapt or pre-train a vision transformer with SSL (e.g. Curia or DINOv2 from scratch) on brain MRI images leveraging large public and private datasets
- Investigate and apply parameter-efficient fine-tuning (PEFT) methods, such as LoRA, to adapt the pre-trained model for specific MS tasks
- Learn everything about the diagnosis of MS on brain MRI (McDonald Criteria, Central Vein Sign (CVS) and paramagnetic rim lesions (PRLs), …)
- Work directly with radiologists to gain feedback and validate model performance against clinical ground truth
- Establish robust evaluation pipelines to quantitatively assess model performance and benchmarking against leading foundation models
In this internship, you will:
You
- Finishing MSc in Mathematics / Computer Science / Electrical Engineering;
- Proficiency with statistics / Machine Learning algorithms
- Proficiency with Deep Learning, Computer Vision algorithms and/or NLP algorithms
- Proficiency with Python
- Willingness to work in the healthcare industry
- Authorization to work in France
Pluses
- Experience in an agile work environment
- Publication history in top-tier Machine Learning or healthcare journals
- Experience in healthcare and/or with the manipulation of radiological images
- Experience with Self-Supervised Learning and Transformers
- Experience with HPC clusters and GPU-based infrastructures
Why join us
- Have a real and positive impact on people's lives
- Apply and improve the latest Deep Learning technologies to build an inspiring product
- Be in contact with radiologists in our partner center
- Be among the first employees of Raidium
- Work with a team of internationally recognized experts
- Work with a diverse team of engineers and medical doctors
- Competitive salary
- Access to premium computing hardware
€2,000 - €2,000 a month
Duration: 6 months
Expected start date: March 2026
Location: Hôpital Cochin, Paris 14