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| # Technical Skills and Expertise | |
| ## Deep Learning and Machine Learning | |
| ### Core Frameworks | |
| - **PyTorch**: Advanced proficiency in model development, custom layers, and distributed training | |
| - **TensorFlow**: Experience with TensorFlow 2.x, Keras, and TensorFlow Serving | |
| - **Hugging Face Transformers**: Fine-tuning, model deployment, and custom tokenizers | |
| - **scikit-learn**: Classical ML algorithms, preprocessing, and model evaluation | |
| ### Specialized Techniques | |
| - **Transfer Learning**: Pre-trained model adaptation, domain adaptation | |
| - **Attention Mechanisms**: Self-attention, cross-attention, multi-head attention | |
| - **Adversarial Training**: GANs, adversarial autoencoders, robust training | |
| - **Multi-task Learning**: Joint optimization, task balancing, shared representations | |
| - **Meta-Learning**: Few-shot learning, model-agnostic meta-learning | |
| ## Large Language Models and NLP | |
| ### LLM Technologies | |
| - **Parameter-Efficient Fine-tuning**: LoRA, QLoRA, AdaLoRA, Prefix tuning | |
| - **Quantization**: GPTQ, GGUF, 8-bit and 4-bit quantization | |
| - **Model Optimization**: Pruning, distillation, efficient architectures | |
| - **Prompt Engineering**: Chain-of-thought, few-shot prompting, instruction tuning | |
| ### NLP Applications | |
| - **Text Generation**: Controlled generation, style transfer, summarization | |
| - **Information Extraction**: Named entity recognition, relation extraction | |
| - **Question Answering**: Reading comprehension, open-domain QA | |
| - **Sentiment Analysis**: Aspect-based sentiment, emotion detection | |
| ## Computer Vision and Medical Imaging | |
| ### Vision Architectures | |
| - **Convolutional Networks**: ResNet, DenseNet, EfficientNet, Vision Transformers | |
| - **Object Detection**: YOLO, R-CNN family, DETR | |
| - **Segmentation**: U-Net, Mask R-CNN, Segment Anything Model (SAM) | |
| - **Medical Imaging**: Specialized architectures for histopathology, radiology | |
| ### Image Processing | |
| - **Preprocessing**: Normalization, augmentation, color space conversion | |
| - **Feature Extraction**: SIFT, HOG, deep features | |
| - **Registration**: Image alignment, geometric transformations | |
| - **Quality Assessment**: Blur detection, artifact identification | |
| ## Multimodal AI and Fusion | |
| ### Multimodal Architectures | |
| - **Vision-Language Models**: CLIP, BLIP, LLaVA, DALL-E | |
| - **Fusion Strategies**: Early fusion, late fusion, attention-based fusion | |
| - **Cross-modal Retrieval**: Image-text matching, semantic search | |
| - **Multimodal Generation**: Text-to-image, image captioning | |
| ### Data Integration | |
| - **Heterogeneous Data**: Combining images, text, tabular data | |
| - **Temporal Fusion**: Time-series integration, sequential modeling | |
| - **Graph Neural Networks**: Relational data modeling, knowledge graphs | |
| ## Retrieval-Augmented Generation (RAG) | |
| ### Vector Databases | |
| - **FAISS**: Efficient similarity search, index optimization | |
| - **ChromaDB**: Document storage and retrieval | |
| - **Weaviate**: Vector search with filtering | |
| - **Milvus**: Scalable vector database management | |
| ### Retrieval Techniques | |
| - **Dense Retrieval**: Bi-encoder architectures, contrastive learning | |
| - **Sparse Retrieval**: BM25, TF-IDF, keyword matching | |
| - **Hybrid Search**: Combining dense and sparse methods | |
| - **Re-ranking**: Cross-encoder models, relevance scoring | |
| ### RAG Optimization | |
| - **Chunk Strategies**: Document segmentation, overlap handling | |
| - **Embedding Models**: Sentence transformers, domain-specific embeddings | |
| - **Query Enhancement**: Query expansion, reformulation | |
| - **Context Management**: Relevance filtering, context compression | |
| ## Bioinformatics and Computational Biology | |
| ### Genomics | |
| - **Sequence Analysis**: Alignment algorithms, variant calling | |
| - **Gene Expression**: RNA-seq analysis, differential expression | |
| - **Pathway Analysis**: Enrichment analysis, network biology | |
| - **Population Genetics**: GWAS, linkage analysis | |
| ### Proteomics | |
| - **Protein Structure**: Structure prediction, folding analysis | |
| - **Mass Spectrometry**: Data processing, protein identification | |
| - **Protein-Protein Interactions**: Network analysis, functional prediction | |
| ### Systems Biology | |
| - **Network Analysis**: Graph theory, centrality measures | |
| - **Mathematical Modeling**: Differential equations, stochastic models | |
| - **Multi-omics Integration**: Data fusion, pathway reconstruction | |
| ## Cloud Computing and MLOps | |
| ### Cloud Platforms | |
| - **AWS**: EC2, S3, SageMaker, Lambda, ECS | |
| - **Google Cloud**: Compute Engine, Cloud Storage, Vertex AI | |
| - **Azure**: Virtual Machines, Blob Storage, Machine Learning Studio | |
| ### MLOps Tools | |
| - **Model Versioning**: MLflow, DVC, Weights & Biases | |
| - **Containerization**: Docker, Kubernetes, container orchestration | |
| - **CI/CD**: GitHub Actions, Jenkins, automated testing | |
| - **Monitoring**: Model drift detection, performance monitoring | |
| ### Distributed Computing | |
| - **Parallel Processing**: Multi-GPU training, data parallelism | |
| - **Cluster Computing**: Spark, Dask, distributed training | |
| - **Resource Management**: SLURM, job scheduling, resource optimization | |
| ## Programming and Software Development | |
| ### Programming Languages | |
| - **Python**: Advanced proficiency, scientific computing, web development | |
| - **R**: Statistical analysis, bioinformatics packages, visualization | |
| - **SQL**: Database design, query optimization, data warehousing | |
| - **JavaScript/TypeScript**: Web development, Node.js, React | |
| - **Bash/Shell**: System administration, automation scripts | |
| ### Development Tools | |
| - **Version Control**: Git, GitHub, collaborative development | |
| - **IDEs**: VS Code, PyCharm, Jupyter notebooks | |
| - **Documentation**: Sphinx, MkDocs, technical writing | |
| - **Testing**: Unit testing, integration testing, test-driven development | |
| ## Research and Academic Skills | |
| ### Research Methodology | |
| - **Experimental Design**: Hypothesis testing, statistical power analysis | |
| - **Literature Review**: Systematic reviews, meta-analysis | |
| - **Peer Review**: Journal reviewing, conference reviewing | |
| - **Grant Writing**: Research proposals, funding applications | |
| ### Communication | |
| - **Technical Writing**: Research papers, documentation, tutorials | |
| - **Presentations**: Conference talks, poster presentations | |
| - **Teaching**: Course development, student mentoring | |
| - **Collaboration**: Interdisciplinary research, team leadership |