We are seeking a highly motivated Postdoc to join the Epilepsy program at Clinatec, France (Grenoble). The fellowship will investigate algorithmic approaches to detecting pre-ictal phenomena (interictal spikes) associated with the occurrence of seizures, using the PNH-penicillin model or other acute models. Seizure prediction is an essential part of the cooling approach-maximizing efficacy while reducing technical constraints. PhD or equivalent with strong knowledge in time-series, machine learning, signal processing, with strong skills in Python. Some experience in manipulating large data and working on clusters (slurm) is an advantage. Otherwise, a basic knowledge of the Unix command line basics is required. Ability to work autonomously.

The position will be for 3 years and open now. Please send your expression of interest to malvina.billeres@cea.fr and get in touch if you have any questions. We look forward to hearing from you!

Thanks

Dr Costecalde Thomas

Here is the full description :

Title: Post-doctoral fellow (H/F) signal processing, AI and software for a closed-loop epilepsy treatment application using focal cooling, seizure prediction and detection.

Context:

Epilepsy remains one of the most common neurological disease. This pathology has an incidence of 50 to 100 000 new cases per year and affects around 50 million people worldwide.Antiepileptics drugs are

currently the gold treatment but they are effective only in 60-70% of patients. For several years, nonpharmacological potential new treatments have been investigated including an interesting new approach

using focal cooling. In the literature, some results in preclinical models have shown that a cooling at 21°C can reduce the frequency of epileptic seizures.

The postdoctoral fellowship will be carried out at Clinatec, in Grenoble, France. We have started a programme focused on focal cooling treatment. Our aim is to develop a medical device delivering a focal

cooling to deep brain regions such as the hippocampus to treat epilepsy. We have therefore developed a preclinical implantable device to deliver this cooling in order to obtain information concerning the

parameters needed to reduce seizures (target temperature, duration…).

The fellowship will investigate algorithmic approaches to detecting pre-ictal phenomena (interictal spikes) associated with the occurrence of seizures, using the PNH-penicillin model or other acute

models. Seizure prediction is an essential part of the cooling approach-maximizing efficacy while reducing technical constraints. The candidate will work closely with a neurosurgeon, who will provide

neuroscientigic expertise, as well as a PhD student (biology). The laboratory also includes engineers, clinicians and a signal processing team.

Missions will include,

• Labelling of the different brain signals occurring in epilepsy (seizure, pre-ictal, interictal, post-ictal…).

• Statistical analysis and hypothesis testing of the data.

• Online closed loop optimization of seizure prediction/forecasting algorithms.

• Detection of anomalies (HFO, fast ripples, phase coupling) for prediction and forecasting.

• Testing of adaptive algorithms for prediction and forecasting (preclinical/clinical database): effects

of cooling on algorithm performance.

• Integration of other measurements (temperature…) in algorithm prediction.

• Maintenance and improvement of experiment software

Profil of candidate:

PhD or equivalent with strong knowledge in time-series, machine learning, signal processing, with strong skills in Python. Some experience in manipulating large data and working on clusters (slurm) is an

advantage. Otherwise, a basic knowledge of the Unix command line basics is required. Ability to work autonomously.