À propos de Niels
PhD in Data Science applied to Astrophysics with a background in Machine Learning, Statistical Modeling, and Time Series Analysis. Skilled in developing Python-based models to analyse complex datasets. Experienced in unsupervised learning (PCA, GMM, K-means, DBSCAN) and computational optimisation. Proficient in Python (NumPy, SciPy, Scikit-learn). Passionate about data-driven decision making and cross-disciplinary collaboration.
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 50 km)
Expériences
- Lund ObservatoryData Scientist for Astrophysics (PhD)octobre 2021 - octobre 2024 (3 ans)Lund, Suède• Worked extensively in computational astrophysics, in a multilingual and multicultural environment• Extensive analysis of many astrophysical large observation datasets with a wide range of data formats and sources• Daily use of Python 3.8, statistical analysis, GMM, PCA, as well as data visualisation techniques and tools• Developed a time series-based classification algorithm in Python for classifying stellar orbits by using their orbital frequencies• Implemented Python scripts for parsing astrophysics specific datasets formats (FITS format)• Implement IDL and Python scripts for measuring models fitting to actual observation data (numpy, pandas)• Analysed large amounts ofsynthetic data from numerical simulations of galaxy formation (Python, pynbody, matplotlib)• Developed basic shell scripts and scheduling using oar batch scheduler• Daily usage of Linux servers for local and remote operations (local and server computation on High Performance Computers)• Mentored Master's degree trainees during their internship (simulation of star orbits using Python 3.8)
- Institut de Planétologie et d'Astrophysique de Grenoble (IPAG)Python Developermars 2021 - juillet 2021 (4 mois)Grenoble, France• Designed and developed a Python simulation application for simulating stars movement based on new generation telescope• Designed an UML class diagram for modeling the stars ecosystem• Implemented application in Python 3.8 using best object-oriented programming practices• Extensive use of SimCADO: a Python library dedicated to Astronomy professionals
- Laboratoire d'Astrophysique de Bordeaux (LAB)Machine Learning in Pythonmai 2020 - juillet 2020 (2 mois)Bordeaux, France• Perform a study ofstar clusters based on unsupervised Machine Learning techniques• Extensive use of scikit-learn library in Python and matplotlib for data visualisation (IDE: spyder; numpy, pandas)• Developed python application for kmeans and dbscan params and features engineering• Performed params optimisation for kmeans and dbscan (elbow method, silhouette coefficient, gap statistics)• Designed a models comparison methodology for comparing models performance on Gaia data
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Formations
- Dual PhD in Data science applied to AstrophysicsLund University (Sweden) and Université Côte d'Azur (France)2024Dual PhD in Data science applied to Astrophysics
- Master's Degree in Computational and Statistical AstrophysicsObservatoire de Paris, Université de Paris-CitéMaster's Degree in Computational and Statistical Astrophysics