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Laatste vacatures

PhD candidate 'Elucidating the molecular etiology of craniofacial malformations in zebrafish models'.

Our research is aimed at elucidating the molecular etiology of cleft palate and other craniofacial malformations in mouse and zebrafish models. In this project, mutations associated with human cleft palate will be introduced into zebrafish using CRISPR/Cas9 gene editing. These mutant lines will be used in combination with environmental factors to investigate the molecular mechanisms involved in craniofacial development and craniofacial morphology.

The development of cartilage and bone structures in the larval head will be analyzed using specific stainings and expression analysis of cartilage and bone marker genes. The morphology of the adult head will be analyzed using nanoCT scanning. This project is a collaboration with the department of Animal Ecology and Physiology of the Faculty of Science.

Tasks and responsibilities

  • Selection of interesting cleft palate candidate genes;
  • Introduction of human cleft palate mutations in zebrafish;
  • Genotyping of mutant larvae;
  • Analysis of cartilage and bone development in zebrafish larvae;
  • Writing applications for the ethical committee for animal research (CCD);
  • Analysis of craniofacial morphology in adult fish;
  • Writing of scientific articles;
  • Managing the research project;
  • Collaboration with experts in this field.

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12-11-2018 Radboudumc


PhD candidate 'Early lung cancer detection with CT'

Lung cancer is the most deadly cancer in men and women. Early detection is the best strategy to reduce lung cancer mortality. Lung cancer is visible on a CT scan as a pulmonary nodule. If these are detected and confirmed to be cancer when they are still small, the disease is still local and lung cancer is curable.
The Diagnostic Image Analysis Group has developed state-of-the-art algorithms and software to find pulmonary nodules in CT scans, assess the type, measure their growth and estimate how likely it is that a nodule is malignant (cancerous). This software has been commercialized and is used worldwide in CT lung cancer screening programs. Recent guidelines state that also for CT scans obtained in routine clinical practice, nodules should be identified, measured and followed according to strict guidelines.
The goal of this project is to further develop automated software to do this.

It will be challenging to adapt the algorithms to deal with the much more diverse CT scans from clinical practice, where many abnormalities are visible in the lungs, compared to a screening scenario where the participants do not have complaints and have largely normal looking lungs. You will use deep learning and convolutional neural networks to analyze the scans. A focus will be on temporal analysis, as in most cases we will have multiple CT scans available for a patient, obtained at different time points. We will also focus on standardizing CT quality, in order to be able to process a wide variety of CT scanning protocols. Generative models and adversarial training should be investigated to achieve the standardized CT quality.

Another important part of the project will be the implementation and validation of the software in both Radboudumc and Jeroen Bosch Hospital. Together, both hospitals obtain over 10,000 of chest CT scans every year.

We are looking for an ambitious PhD candidate to work on this four-year project that is funded by both hospitals who are strongly collaborating to implement various artificial intelligence solutions in the radiological routine workflow. You will be embedded in the Diagnostic Image Analysis Group, a large group with around 50 researchers all working on deep learning based medical image analysis.

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12-11-2018 Radboudumc


Associate professor in clinical epidemiology

At the Radboud University Medical Center (Radboudumc) in Nijmegen, The Netherlands, bachelor and master students in biomedical sciences and medicine as well as PhD candidates in health sciences are being trained in research methodology. Clinical epidemiology is at the roots of this research methodology.

The associate professor in clinical epidemiology will be part of a team of epidemiologists, biostatisticians and health technology experts that is responsible for a high level training program in research methods. The team also provides consultation services to the Radboudumc researchers. You will be responsible for the clinical epidemiology teaching program and will also build up a new research line on prediction research with applications primarily in the area in cancer (epidemiology). You report to the head of the Department.

Tasks and responsibilities
You will be active in teaching (~ 0.5fte), consulting and research (~0.5 fte). You will teach clinical epidemiology from the basics up to advanced prediction modeling to (bio)medical students and PhD candidates, mostly from a self-directed learning perspective and during coaching sessions and interactive lectures. For this, you also (co)develop teaching materials. You will be responsible for the clinical epidemiology curriculum content in the two curricula. In consulting, you will aid biomedical researchers in designing studies and analytical approaches. In research, you will build up your own research line by acquiring grants that have a focus on clinical questions in the area of cancer and/or methodological innovations.

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12-11-2018 Radboudumc


PhD candidate 'Early lung cancer detection with CT'

Lung cancer is the most deadly cancer in men and women. Early detection is the best strategy to reduce lung cancer mortality. Lung cancer is visible on a CT scan as a pulmonary nodule. If these are detected and confirmed to be cancer when they are still small, the disease is still local and lung cancer is curable.
The Diagnostic Image Analysis Group has developed state-of-the-art algorithms and software to find pulmonary nodules in CT scans, assess the type, measure their growth and estimate how likely it is that a nodule is malignant (cancerous). This software has been commercialized and is used worldwide in CT lung cancer screening programs. Recent guidelines state that also for CT scans obtained in routine clinical practice, nodules should be identified, measured and followed according to strict guidelines.
The goal of this project is to further develop automated software to do this.

It will be challenging to adapt the algorithms to deal with the much more diverse CT scans from clinical practice, where many abnormalities are visible in the lungs, compared to a screening scenario where the participants do not have complaints and have largely normal looking lungs. You will use deep learning and convolutional neural networks to analyze the scans. A focus will be on temporal analysis, as in most cases we will have multiple CT scans available for a patient, obtained at different time points. We will also focus on standardizing CT quality, in order to be able to process a wide variety of CT scanning protocols. Generative models and adversarial training should be investigated to achieve the standardized CT quality.

Another important part of the project will be the implementation and validation of the software in both Radboudumc and Jeroen Bosch Hospital. Together, both hospitals obtain over 10,000 of chest CT scans every year.

We are looking for an ambitious PhD candidate to work on this four-year project that is funded by both hospitals who are strongly collaborating to implement various artificial intelligence solutions in the radiological routine workflow. You will be embedded in the Diagnostic Image Analysis Group, a large group with around 50 researchers all working on deep learning based medical image analysis.

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09-11-2018 Radboudumc


PhD candidate 'Elucidating the molecular etiology of craniofacial malformations in zebrafish models'.

Our research is aimed at elucidating the molecular etiology of cleft palate and other craniofacial malformations in mouse and zebrafish models. In this project, mutations associated with human cleft palate will be introduced into zebrafish using CRISPR/Cas9 gene editing. These mutant lines will be used in combination with environmental factors to investigate the molecular mechanisms involved in craniofacial development and craniofacial morphology.

The development of cartilage and bone structures in the larval head will be analyzed using specific stainings and expression analysis of cartilage and bone marker genes. The morphology of the adult head will be analyzed using nanoCT scanning. This project is a collaboration with the department of Animal Ecology and Physiology of the Faculty of Science.

Tasks and responsibilities

  • Selection of interesting cleft palate candidate genes;
  • Introduction of human cleft palate mutations in zebrafish;
  • Genotyping of mutant larvae;
  • Analysis of cartilage and bone development in zebrafish larvae;
  • Writing applications for the ethical committee for animal research (CCD);
  • Analysis of craniofacial morphology in adult fish;
  • Writing of scientific articles;
  • Managing the research project;
  • Collaboration with experts in this field.

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08-11-2018 Radboudumc