Jr. Machine Learning Engineer
Radiologics Medical
(< 10 employees)
Keywords
Skills
Background and knowledge in computer programming languages and frameworks including MATLAB, Python, HTML, CSS, JavaScript, React and React Native.
Skilled in working with FEBio (Finite Elements for Biomechanics) software tool
I designed a project in Molecular Modeling of Protein Interactions (Molecular Docking) and Bioinformatics during my graduation thesis.
I gained skills and competencies in Molecular Design with Deep Learning during my internships.
The knowledge, skills and competencies acquired that I worked on during my internships and work experiences:
• Bioinformatic and cheminformatic analysis of the nutraceutical database was conducted and the results of the research were published as a paper.
Kaya, I. and Colmenarejo, G. “Analysis of Nuisance Substructures and Aggregators in a Comprehensive Database of Food Chemical Compounds,” Journal of Agricultural and Food Chemistry, vol. 68, no. 33, pp. 8812–8824, Aug. 2020, doi: 10.1021/acs.jafc.0c02521.
• Deeper knowledge of Python libraries for data analysis and data visualization and chemoinformatic toolkits especially Pandas, NumPy, Matplotlib
• Deeper knowledge of Python libraries for chemoinformatic toolkits: RDKit
• Familiarization with Python Machine Learning and Deep Learning libraries: TensorFlow, Keras, Scikit-Learn
• Gaining a deeper knowledge of Python libraries for working medical imaging datasets: pydicom, SimpleITK
• Gaining a deeper knowledge of Python libraries for preprocessing of the data and image processing: scikit-image, OpenCV
• Gaining knowledge of Python Deep Learning libraries: PyTorch, PyTorch Lightning
• Working on medical image segmentation studies with CNN-based methods
• Professional usage of Git
Skilled in working with FEBio (Finite Elements for Biomechanics) software tool
I designed a project in Molecular Modeling of Protein Interactions (Molecular Docking) and Bioinformatics during my graduation thesis.
I gained skills and competencies in Molecular Design with Deep Learning during my internships.
The knowledge, skills and competencies acquired that I worked on during my internships and work experiences:
• Bioinformatic and cheminformatic analysis of the nutraceutical database was conducted and the results of the research were published as a paper.
Kaya, I. and Colmenarejo, G. “Analysis of Nuisance Substructures and Aggregators in a Comprehensive Database of Food Chemical Compounds,” Journal of Agricultural and Food Chemistry, vol. 68, no. 33, pp. 8812–8824, Aug. 2020, doi: 10.1021/acs.jafc.0c02521.
• Deeper knowledge of Python libraries for data analysis and data visualization and chemoinformatic toolkits especially Pandas, NumPy, Matplotlib
• Deeper knowledge of Python libraries for chemoinformatic toolkits: RDKit
• Familiarization with Python Machine Learning and Deep Learning libraries: TensorFlow, Keras, Scikit-Learn
• Gaining a deeper knowledge of Python libraries for working medical imaging datasets: pydicom, SimpleITK
• Gaining a deeper knowledge of Python libraries for preprocessing of the data and image processing: scikit-image, OpenCV
• Gaining knowledge of Python Deep Learning libraries: PyTorch, PyTorch Lightning
• Working on medical image segmentation studies with CNN-based methods
• Professional usage of Git
Project history
07/2022
-
Present
- Gaining knowledge of Python Deep Learning libraries: PyTorch, PyTorch Lightning
- Gaining a deeper knowledge of Python libraries for preprocessing of the data and image processing: scikit-image, OpenCV
- Working on medical image segmentation studies with CNN-based methods
- Working on lung disease diagnosis studies with Deep Learning
- Professional usage of Git
- Company project reporting
02/2022
-
07/2022
Deep Learning Engineer Intern
Radiologics Medical
(< 10 employees)
- Gaining a deeper knowledge of biomedical imaging datasets and their properties
- Gaining a deeper knowledge of Python libraries for working medical imaging datasets: pydicom, SimpleITK
- Working on medical image segmentation studies with traditional methods
- Working on lung disease diagnosis studies with Deep Learning
Local Availability
Only available in these countries:
Turkey
