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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