23.11.2024 aktualisiert
100 % verfügbar
Machine Learning Scientist with PhD in Computational Acoustics
Berlin, Deutschland
Weltweit
Doctor of Philosophy (PhD)Skills
AkustikAlgorithmus-EntwurfBig DataComputational Fluid DynamicsCloud ComputingComputersimulationComputerprogrammierungInformation EngineeringDatenmanagementFluiddynamikHochfrequenzPythonMATLABMachine LearningMongoDB
Machine Learning and Deep Learning
Expertise in algorithm development for pattern recognition in audio data, using CNN, attention models, and recurrent neural networks.
Data Science and Analysis
Proficiency in statistical audio analysis, data engineering, and implementing various machine learning models including Random Forest and Support Vector Machines.
Programming and Software Development
Strong skills in Python, SQL, MATLAB, and various deep learning frameworks such as PyTorch and TensorFlow.
Computational Acoustics
Specialized knowledge in computational fluid dynamics and engineering acoustics, with focus on nonlinear acoustics and ultrasound.
Signal Processing
Experience in digital signal processing for high frequency acoustic data and time-dependent inversion methods.
Cloud and Big Data Technologies
Familiarity with cloud platforms, big data tools like MongoDB, and containerization technologies such as Docker and Kubernetes.
Scientific Computing
Proficiency in numerical modeling and computational methods for acoustics and fluid dynamics simulations.
Expertise in algorithm development for pattern recognition in audio data, using CNN, attention models, and recurrent neural networks.
Data Science and Analysis
Proficiency in statistical audio analysis, data engineering, and implementing various machine learning models including Random Forest and Support Vector Machines.
Programming and Software Development
Strong skills in Python, SQL, MATLAB, and various deep learning frameworks such as PyTorch and TensorFlow.
Computational Acoustics
Specialized knowledge in computational fluid dynamics and engineering acoustics, with focus on nonlinear acoustics and ultrasound.
Signal Processing
Experience in digital signal processing for high frequency acoustic data and time-dependent inversion methods.
Cloud and Big Data Technologies
Familiarity with cloud platforms, big data tools like MongoDB, and containerization technologies such as Docker and Kubernetes.
Scientific Computing
Proficiency in numerical modeling and computational methods for acoustics and fluid dynamics simulations.
Erfahrung
Berufserfahrung5 - 10 Jahre
Freelancer seit2024
Sprachen
DeutschgutEnglischverhandlungssicherTürkischMuttersprache
Projekthistorie
Self-training in deep learning topics such as speech processing and participation in Kaggle competition BirdClef 2023.
Algorithm development for pattern recognition in audio data, bird species identification using CNN and recurrent neural networks, statistical audio analysis, and data engineering using MySQL and Python.
Numerical and experimental modelling of aero-acoustic noise, development of computational methods for acoustics and signal processing, software development using MATLAB, and teaching Computational Acoustics.