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PhD Position in France
Ph.D. subject: Modeling and classification of cancer cells using biophysical signatures
A Ph.D. grant is offered with financial support by Région Hauts-de-France and Yncréa HdF to perform the entitled project starting on October 1, 2020, for 3 years. The recruited Ph.D. student will join the Digital Systems and Life Sciences (DSLS) department at Yncréa Hauts-de-France (Lille) and research in the BioMEMS team at the Institute of Electronics, Microelectronics and Nanotechnology (IEMN)-UMR 8520. This project relies on the unique environment of the SMMiL-E international project applying BioMEMS technology on cancer studies through interdisciplinary collaboration among Centre Oscar Lambret, The University of Tokyo, CNRS, and the University of Lille.
Description
Most cancer patients die due to the metastatic evolution of the tumor, and the early steps of this complicated process are difficult to detect. Although advanced enrichment techniques exist for disseminated tumor cells, the scarce number and high heterogeneity of these cells require practical new ways to anticipate metastatic development at an early stage of the disease. Biophysical studies have demonstrated that cancer progression involves changes in cell morphology. However, cell biophysical signature has not yet applied to detect cancer cells or to evaluate their metastatic potential. The heterogeneity of cancer cells demands a multi-parameter single-cell analysis. Such an analysis technique requires analyzing a considerable amount of data for a vast number of cells, which makes signal processing and machine learning techniques crucial elements of a practical evaluation method. Thus, this project aims at developing the concept of biophysical phenotype as a cancer cell marker by modeling cell biophysical signature and applying statistical classification methods to detect and evaluate disseminated cancer cells for diagnosis and prognostics.
Keywords: BioMEMS, single-cell analysis, circulating tumor cells, biophysical modeling, signal processing, machine learning, automatic classification.
Objectives
Based on a single cell characterization method (developed at SMMiL-E in collaboration with the University of Lille and INSERM UMR Canther) allowing mechanical characterization of cells while monitoring subcellular components with a confocal microscope, this study targets two main objectives:
- Correlating mechanical properties of cancer cells with their metastatic potential. This correlation will be achieved by measuring the biophysical responses (of cell lines) and applying machine-learning techniques to establish cell pattern recognition. The algorithms will be further trained on more heterogeneous cancer cell populations linked with biological assays to assess their malignancy.
- Developing a biophysical cancer cell model based on the obtained results to simulate the cell response under different dynamic stimulations and to examine how metastatic progression alters the rheology of a cancer cell and its subcellular components.
Profile of the candidate
The applicant must hold (by the beginning of the contract) an MSc degree in a relevant area, such as physics, electrical engineering, bioengineering, biomedical engineering, microtechnology, materials science, and computer science. The applicant should be highly motivated, autonomous, and dynamic to perform the described interdisciplinary research. Knowledge in several key topics, i.e. biology, computer simulation, signal processing, statistical analysis, machine learning, and mathematical modeling, is required, and experience in microtechnologies is appreciated. Proficiency in English is essential.
Further information
A brut salary of 1856€ per month. The application (CV, motivation letter, recommendation letter(s), and transcript) to be submitted before May 17, 2020 (Paris Time). For applications and questions, please contact Cagatay TARHAN (cagatay.tarhan@yncrea.fr) and Hua CAO (hua.cao@yncrea.fr).
A Ph.D. grant is offered with financial support by Région Hauts-de-France and Yncréa HdF to perform the entitled project starting on October 1, 2020, for 3 years. The recruited Ph.D. student will join the Digital Systems and Life Sciences (DSLS) department at Yncréa Hauts-de-France (Lille) and research in the BioMEMS team at the Institute of Electronics, Microelectronics and Nanotechnology (IEMN)-UMR 8520. This project relies on the unique environment of the SMMiL-E international project applying BioMEMS technology on cancer studies through interdisciplinary collaboration among Centre Oscar Lambret, The University of Tokyo, CNRS, and the University of Lille.
Description
Most cancer patients die due to the metastatic evolution of the tumor, and the early steps of this complicated process are difficult to detect. Although advanced enrichment techniques exist for disseminated tumor cells, the scarce number and high heterogeneity of these cells require practical new ways to anticipate metastatic development at an early stage of the disease. Biophysical studies have demonstrated that cancer progression involves changes in cell morphology. However, cell biophysical signature has not yet applied to detect cancer cells or to evaluate their metastatic potential. The heterogeneity of cancer cells demands a multi-parameter single-cell analysis. Such an analysis technique requires analyzing a considerable amount of data for a vast number of cells, which makes signal processing and machine learning techniques crucial elements of a practical evaluation method. Thus, this project aims at developing the concept of biophysical phenotype as a cancer cell marker by modeling cell biophysical signature and applying statistical classification methods to detect and evaluate disseminated cancer cells for diagnosis and prognostics.
Keywords: BioMEMS, single-cell analysis, circulating tumor cells, biophysical modeling, signal processing, machine learning, automatic classification.
Objectives
Based on a single cell characterization method (developed at SMMiL-E in collaboration with the University of Lille and INSERM UMR Canther) allowing mechanical characterization of cells while monitoring subcellular components with a confocal microscope, this study targets two main objectives:
- Correlating mechanical properties of cancer cells with their metastatic potential. This correlation will be achieved by measuring the biophysical responses (of cell lines) and applying machine-learning techniques to establish cell pattern recognition. The algorithms will be further trained on more heterogeneous cancer cell populations linked with biological assays to assess their malignancy.
- Developing a biophysical cancer cell model based on the obtained results to simulate the cell response under different dynamic stimulations and to examine how metastatic progression alters the rheology of a cancer cell and its subcellular components.
Profile of the candidate
The applicant must hold (by the beginning of the contract) an MSc degree in a relevant area, such as physics, electrical engineering, bioengineering, biomedical engineering, microtechnology, materials science, and computer science. The applicant should be highly motivated, autonomous, and dynamic to perform the described interdisciplinary research. Knowledge in several key topics, i.e. biology, computer simulation, signal processing, statistical analysis, machine learning, and mathematical modeling, is required, and experience in microtechnologies is appreciated. Proficiency in English is essential.
Further information
A brut salary of 1856€ per month. The application (CV, motivation letter, recommendation letter(s), and transcript) to be submitted before May 17, 2020 (Paris Time). For applications and questions, please contact Cagatay TARHAN (cagatay.tarhan@yncrea.fr) and Hua CAO (hua.cao@yncrea.fr).