ADAGOS is participating in several local, national and European projects in the field of health. It has developed a new parsimonious approach that reduces the resources (including energy) required for implementing artificial intelligence algorithms by orders of magnitude. The goal is to create real time patient specific models to support clinical decision making.
The patient specific model, or the “digital twin” of the patient, allows for the comparison of different scenarios in a virtual environment, enabling clinicians to make the best decision for the patient in full knowledge of all the impacts and benefits of the different scenarios.
We are seeking a talented R&D engineer with a background in applied mathematics. She or he should join our outstanding parsimonious convolutional neural networks team to contribute in developing efficient algorithms for health applications.
- Contribute in designing, implementing and validating parsimonious neural networks algorithms
- Participate and collaborate with our European partners in the fields of health applications
- Presentation (verbal and written) in English of findings, contributions to internal reports, invention disclosures and external publications and conference presentations.
- Ideally a PhD in applied mathematics with a scientific computing background.
- Experience in inverse problems, data assimilation, control theory, optimization or algorithmic differentiation, would be appreciated, but there is no need to have particular skills in data science or statistics.
- Creativity, exceptional critical thinking and problem-solving skills that promote a goal-oriented work culture.
- Capable to adapt to face new challenges.
- Current authorization to work in France.
Type of contract: Permanent
City: Toulouse (France)
Please submit your resume and a cover letter detailing your interest and experience to email@example.com. ADAGOS is offering a competitive salary and benefits package.