14.09.2025 aktualisiert
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Ph.D. Researcher in Machine Learning and Graph Neural Networks
Bonn, Deutschland Ph.D. in Computer Science (submitted)
Skills
AlgorithmusData AnalysisKünstliche Neurale NetzwerkeBiologieDistributed ComputingMachine LearningNatural Language ProcessingTensorflowTransformerWorkflowsBestärkendes LernenPytorchDeep LearningKerasDaten-Pipeline
Machine Learning
Expertise in developing and implementing machine learning models with focus on graph-based models for genomic data assembly and natural language processing.
Graph Neural Networks
Specialized knowledge in Graph Neural Networks, Graph Transformers, and path-finding algorithms for solving complex assembly graph problems.
Reinforcement Learning
Advanced application of reinforcement learning techniques for distributed systems and solving computational challenges in bioinformatics.
Deep Learning Frameworks
Proficiency with PyTorch, TensorFlow, and Keras for implementing and deploying machine learning solutions.
Contrastive Learning
Application of contrastive learning methods for improved representation learning in biological data analysis.
Data Pipeline Development
Design and implementation of end-to-end pipelines for processing large-scale biological data and ML training workflows.
Natural Language Processing
Development of deep learning models for NLP applications in both industry and research contexts.
Expertise in developing and implementing machine learning models with focus on graph-based models for genomic data assembly and natural language processing.
Graph Neural Networks
Specialized knowledge in Graph Neural Networks, Graph Transformers, and path-finding algorithms for solving complex assembly graph problems.
Reinforcement Learning
Advanced application of reinforcement learning techniques for distributed systems and solving computational challenges in bioinformatics.
Deep Learning Frameworks
Proficiency with PyTorch, TensorFlow, and Keras for implementing and deploying machine learning solutions.
Contrastive Learning
Application of contrastive learning methods for improved representation learning in biological data analysis.
Data Pipeline Development
Design and implementation of end-to-end pipelines for processing large-scale biological data and ML training workflows.
Natural Language Processing
Development of deep learning models for NLP applications in both industry and research contexts.
Sprachen
DeutschMutterspracheEnglischverhandlungssicher
Projekthistorie
Machine learning research for large-scale biological data, focusing on graph-based models for de novo genome assembly. Designed end-to-end pipelines for data processing and ML training on real-world genomic data.
Built and deployed deep learning models for natural language processing (NLP) for industry and research projects.
Provided private tutoring in mathematics for high school students.