Auto-adaptive neural decoder for clinical brain-spine interfacing
CEA/LETI/CLINATEC invite applications for postdoctoral position to work on the HORIZON-EIC project. The project goal is to explore novel solutions for functional rehabilitation and/or compensation for people with sever motor disabilities using auto-adaptive Brain-Machine Interface (BMI) / neuroprosthetics. Neuroprosthetics record, and decode brain neuronal signal for activating effectors (exoskeleton, implantable spinal cord stimulator etc.) directly without physiological neural control command pass way interrupted by spinal cord injury. A set of algorithms to decode neuronal activity recorded at the level of the cerebral cortex (Electrocorticogram) using chronic WIMAGINE implants were developed at CLINATEC and tested in the frame of 2 clinical research protocols in tetraplegics in Grenoble and in paraplegics in Lausanne. The postdoctoral fellow will contribute to the next highly ambitious scientific breakthroughs addressing the medical needs of patients. The crucial improvement of usability may be achieved by alleviating the need of constant BMI decoder recalibration introducing an auto-adaptive framework to train the decoder in an adaptive manner during the neuroprosthetics self-directed use. Auto-adaptive BMI (A-BMI) adds a supplementary loop evaluating from neuronal data the level of coherence between user’s intended motions and effector actions. It may provide BMI task information (labels) to the data registered during the neuroprosthetics self-directed use to be employed for BMI decoder real-time update. Innovative A-BMI neural decoder will be explored and tested offline and in real-time in ongoing clinical trials.
Post doctoral/Research engineer position in esophageal tissue engineering using bioprinting techniques
Due to disease such as cancer or accidents such as caustic burns, the esophagus is sometimes irreversibly damaged and the only option is to remove it and replace it by using the stomach and part of the digestive tract, which often leads to serious complications and even in the best cases to poor functional results and poor quality of life. The most advanced current developments in tissue engineering for the esophagus is the use of decellularized donor tissue and clinical trials are ongoing at St Louis Hospital in this area. This approach however still presents some limitations, in particular related to donor shortage and inflammatory response. In order to prepare the next generation approach, the lab initiated a project funded by MSD Avenir to build an esophagus substitute using 3D printing. This bottom-up approach which uses bioinks as a starting material allows full control over 3D architecture and the construct can be thus personalized to the patient’s morphology and pathology, including smaller sizes for pediatric patients, in unlimited supply which is a great advantage over donor tissue. We have patented a formulation based on both natural and synthetic polymers which shows similar mechanical properties when compared to native esophagi, good suturability as well as high porosity to allow cell colonization. It also presents slow degradation as the ultimate aim is that it be replaced with native regenerated tissue over time.
Profile
We are seeking a highly motivated and autonomous post-doctoral fellow or research engineer to continue this project and characterize long term culture on this scaffold by re-epithelializing the interior of the tube and seeding primary endothelial and muscular cells on the outer part. Characterizations will include both material mechanical testing and long term cell behavior, morphology and analysis of any toxicity.
Location; St Louis Hospital, Paris
Postdoctoral fellow in AI, real time signal processing and software for real time epilepsy prediction/forecasting for closed loop neuromodulation by focal Cooling.
To date seizure suppression stimulation technologies (electrical stimulation) are majorly based on seizure detection procedure. No study has provided sound evidence that prospective seizure prediction/forecasting can be used to trigger closed loop therapeutics for drug resistant epilepsy treatment. Our proposal is based on the existing motor brain-computer interface algorithms already in clinical use. They can be adapted to generate prediction/forecasting of seizures occurrence. Our working hypothesis is that treating during high-risk seizures periods and not during the actual seizure would require relatively minor doses of the therapeutical element. This will reduce the power consumption and open the door to fully implantable system. Decoding algorithms will be potentially redesigned to respond better to the epileptic seizures forecasting task. They will be compared to the state of the art CNN based approaches, and other approaches. Prediction/forecasting seizures algorithms will be evaluated in an epilepsy model established at Clinatec, using non-human primates, and the algorithms will be refined over time. Cooling the epileptic foci is an effective way to stop de seizure before generalization. This model allows us to test the efficacy of the algorithms in treating focal seizures. An assessment of hardware embedding design constraints would be conducted to facilitate next steps for the clinical device development. The project will benefit from a collaboration between Clinatec and DSYS/SSCE; and will be in line with upcoming activities of LETI’s artificial intelligence platform.
Fast-scintillator-based device for on-line FLASH-beam dosimetry
New cancer treatment modalities aim to improve the dose delivered to the tumor while sparing healthy tissue as much as possible. Various approaches are being developed, including the temporal optimization of the dose delivered with very high dose rate irradiation (FLASH).
In this particular case, recent studies have shown that FLASH irradiation with electrons was as effective as photon beam treatments for tumor destruction while being less harmful to healthy tissue. For these beams, the instantaneous doses are up to several orders of magnitude higher than those produced by conventional beams. Conventional active dosimeters saturate under irradiation conditions at very high dose rates per pulse, therefore on-line dosimetry of the beam is not possible.
We propose to develop a dosimeter dedicated to the measurement of beams in FLASH radiotherapy based on an ultra-fast plastic scintillator coupled with a silicon photomultiplier sensor (SiPM). The novelty of the project lies both in the chemical composition of the plastic scintillator which will be chosen for its response time and its wavelength emission to have a response adapted to the impulse characteristics of the beam, and in the final sensor with the possibility of coupling the plastic scintillator to a miniaturized SiPM matrix.
The final goal is to be able to access, with a reliable methodology, the dosimetry and in-line geometry of FLASH beams.
Measurement of active cell nematics by lensless microscopy
At CEA-Leti we have validated a video-lens-free microscopy platform by performing thousands of hours of real-time imaging observing varied cell types and culture conditions (e.g.: primary cells, human stem cells, fibroblasts, endothelial cells, epithelial cells, 2D/3D cell culture, etc.). And we have developed different algorithms to study major cell functions, i.e. cell adhesion and spreading, cell division, cell division orientation, and cell death.
The research project of the post-doc is to extend the analysis of the datasets produced by lens-free video microscopy. The post-doc will assist our partner in conducting the experimentations and will develop the necessary algorithms to reconstruct the images of the cell culture in different conditions. In particular, we will challenge the holographic reconstruction algorithms with the possibility to quantify the optical path difference (i.e. the refractive index multiplied by the thickness). Existing algorithms allow to quantify isolated cells. They will be further developed and assessed to quantify the formation of cell stacking in all three dimensions. These algorithms will have no Z-sectioning ability as e.g. confocal microscopy, only the optical path thickness will be measured.
We are looking people who have completed a PhD in image processing and/or deep learning with skills in the field of microscopy applied to biology.